Goggans et al., INSERT JOURNAL 2021, Microbiome Analysis Code

1 Introduction

INSERT ABSTRACT

Our final taxa used in this anaylsis: - included from Bacteria, Archaea, Eukaryota and viruses - removed Chordata, Arthropoda, Cnidaria, Porifera, Echinodermata, Streptophyta, Platyhelminthes because they are implausible in our biological system (pig gut microbiome) - remove genera/phyla that have more than 20 zeroes/33.33% missing values across our dataset of n=60

In the end: Our final dataset has 45 phyla and 755 genera.

1.0.1 Load libraries

# analysis packages
library(ALDEx2) # for univariate analysis
library(rstatix) # for ANOVA
library(vegan) # for beta and alpha diversity
library(phyloseq) # for krona plots and rarefaction curves
library(psadd) # additions to phyloseq package for microbiome analysis
library(ranacapa) # Utility Functions  for Simple Environmental Visualizations

# functionality packages
library(data.table) # for nicer transposing
library(here) # for directory management
library(knitr) # for knitting and for kable()
library(tidyverse) # for wrangling and plotting
library(readxl) # for reading Excel files

1.0.2 Set seed

Some of our analyses include permutations, so let’s set a seed so we get consistent results each time we run.

set.seed(2021) # hoping this seed is better than 2020 :)

1.0.3 Read in metadata

Input files can be found as supplementary information in:

• UPDATE WITH MALLORY’S PAPER INFO

The data read in chunk below enables loading our data without any outside-of-R handling. In “Metadata” tab of Supplementary Information.

# upload metadata
AllSamples.Metadata <- read_excel("Goggans_etal_2021_tomato_pig_microbiome_WGS.xlsx",
                                       sheet = "SampleMetadata")

str(AllSamples.Metadata)

## tibble [60 × 5] (S3: tbl_df/tbl/data.frame)
##  $ Sample_Name       : chr [1:60] "ShotgunWGS-ControlPig6GutMicrobiome-Day14" "ShotgunWGS-ControlPig8GutMicrobiome-Day0" "ShotgunWGS-ControlPig3GutMicrobiome-Day14" "ShotgunWGS-TomatoPig14GutMicrobiome-Day7" ...
##  $ Pig               : num [1:60] 6 8 3 14 5 18 16 10 2 18 ...
##  $ Diet              : chr [1:60] "Control" "Control" "Control" "Tomato" ...
##  $ Time_Point        : chr [1:60] "Day 14" "Day 0" "Day 14" "Day 7" ...
##  $ Diet_By_Time_Point: chr [1:60] "Control Day 14" "Control Day 0" "Control Day 14" "Tomato Day 7" ...

# convert Pig, Diet, Time_Point, Diet_By_Time_Point to factors
# and set levels/order
AllSamples.Metadata$Pig <- as.factor(AllSamples.Metadata$Pig)
AllSamples.Metadata$Diet <- as.factor(AllSamples.Metadata$Diet)
AllSamples.Metadata$Time_Point <- factor(AllSamples.Metadata$Time_Point,
                                         levels = c("Day 0", "Day 7", "Day 14"))
AllSamples.Metadata$Diet_By_Time_Point <- 
  factor(AllSamples.Metadata$Diet_By_Time_Point,
         levels = c("Control Day 0", 
                  "Control Day 7", 
                  "Control Day 14", 
                  "Tomato Day 0", 
                  "Tomato Day 7", 
                  "Tomato Day 14"))

# check
str(AllSamples.Metadata)

## tibble [60 × 5] (S3: tbl_df/tbl/data.frame)
##  $ Sample_Name       : chr [1:60] "ShotgunWGS-ControlPig6GutMicrobiome-Day14" "ShotgunWGS-ControlPig8GutMicrobiome-Day0" "ShotgunWGS-ControlPig3GutMicrobiome-Day14" "ShotgunWGS-TomatoPig14GutMicrobiome-Day7" ...
##  $ Pig               : Factor w/ 20 levels "1","2","3","4",..: 6 8 3 14 5 18 16 10 2 18 ...
##  $ Diet              : Factor w/ 2 levels "Control","Tomato": 1 1 1 2 1 2 2 1 1 2 ...
##  $ Time_Point        : Factor w/ 3 levels "Day 0","Day 7",..: 3 1 3 2 2 2 2 2 1 1 ...
##  $ Diet_By_Time_Point: Factor w/ 6 levels "Control Day 0",..: 3 1 3 5 2 5 5 2 1 4 ...

2 Genera-level annotation

Read in genera level data, annotated from MG-RAST. In “Genera” tab of Supplementary Information.

Genus.AllSamples.Counts <- read_excel("Goggans_etal_2021_tomato_pig_microbiome_WGS.xlsx",
                                       sheet = "Genera")

str(Genus.AllSamples.Counts)

## tibble [1,085 × 66] (S3: tbl_df/tbl/data.frame)
##  $ domain                                    : chr [1:1085] "Viruses" "Bacteria" "Eukaryota" "Bacteria" ...
##  $ phylum                                    : chr [1:1085] "unclassified (derived from Viruses)" "Firmicutes" "unclassified (derived from Eukaryota)" "Cyanobacteria" ...
##  $ class                                     : chr [1:1085] "unclassified (derived from Viruses)" "Bacilli" "unclassified (derived from Eukaryota)" "unclassified (derived from Cyanobacteria)" ...
##  $ order                                     : chr [1:1085] "Caudovirales" "Lactobacillales" "unclassified (derived from Eukaryota)" "unclassified (derived from Cyanobacteria)" ...
##  $ family                                    : chr [1:1085] "Podoviridae" "Aerococcaceae" "unclassified (derived from Eukaryota)" "unclassified (derived from Cyanobacteria)" ...
##  $ genus                                     : chr [1:1085] "AHJD-like viruses" "Abiotrophia" "Acanthamoeba" "Acaryochloris" ...
##  $ ShotgunWGS-ControlPig6GutMicrobiome-Day14 : num [1:1085] 29 5067 0 271 1988 ...
##  $ ShotgunWGS-ControlPig8GutMicrobiome-Day0  : num [1:1085] 0 5661 0 416 2981 ...
##  $ ShotgunWGS-ControlPig3GutMicrobiome-Day14 : num [1:1085] 153 4117 0 267 2071 ...
##  $ ShotgunWGS-TomatoPig14GutMicrobiome-Day7  : num [1:1085] 0 1576 1 131 1012 ...
##  $ ShotgunWGS-ControlPig5GutMicrobiome-Day7  : num [1:1085] 14 3708 0 230 1991 ...
##  $ ShotgunWGS-TomatoPig18GutMicrobiome-Day7  : num [1:1085] 1 1159 0 146 585 ...
##  $ ShotgunWGS-TomatoPig16GutMicrobiome-Day7  : num [1:1085] 2 2495 0 133 1538 ...
##  $ ShotgunWGS-ControlPig10GutMicrobiome-Day7 : num [1:1085] 0 1636 0 141 812 ...
##  $ ShotgunWGS-ControlPig2GutMicrobiome-Day0  : num [1:1085] 0 4534 0 338 2670 ...
##  $ ShotgunWGS-TomatoPig18GutMicrobiome-Day0  : num [1:1085] 0 2964 1 272 1665 ...
##  $ ShotgunWGS-ControlPig10GutMicrobiome-Day0 : num [1:1085] 0 3197 0 264 1411 ...
##  $ ShotgunWGS-ControlPig7GutMicrobiome-Day0  : num [1:1085] 0 2513 0 263 1652 ...
##  $ ShotgunWGS-ControlPig8GutMicrobiome-Day14 : num [1:1085] 342 4231 0 274 1795 ...
##  $ ShotgunWGS-TomatoPig11GutMicrobiome-Day0  : num [1:1085] 0 3101 0 237 2160 ...
##  $ ShotgunWGS-TomatoPig19GutMicrobiome-Day0  : num [1:1085] 0 3274 0 228 1729 ...
##  $ ShotgunWGS-TomatoPig17GutMicrobiome-Day14 : num [1:1085] 6 1424 0 83 683 ...
##  $ ShotgunWGS-ControlPig9GutMicrobiome-Day14 : num [1:1085] 131 3337 0 328 1722 ...
##  $ ShotgunWGS-ControlPig10GutMicrobiome-Day14: num [1:1085] 86 3383 0 238 1976 ...
##  $ ShotgunWGS-TomatoPig19GutMicrobiome-Day7  : num [1:1085] 0 1849 0 120 940 ...
##  $ ShotgunWGS-ControlPig5GutMicrobiome-Day14 : num [1:1085] 76 3864 0 363 2395 ...
##  $ ShotgunWGS-ControlPig2GutMicrobiome-Day7  : num [1:1085] 1 5590 0 306 4493 ...
##  $ ShotgunWGS-ControlPig6GutMicrobiome-Day7  : num [1:1085] 0 3120 0 201 1273 ...
##  $ ShotgunWGS-TomatoPig12GutMicrobiome-Day0  : num [1:1085] 0 2599 0 190 1451 ...
##  $ ShotgunWGS-TomatoPig14GutMicrobiome-Day0  : num [1:1085] 1 1453 0 70 846 ...
##  $ ShotgunWGS-ControlPig7GutMicrobiome-Day14 : num [1:1085] 67 2906 0 248 1870 ...
##  $ ShotgunWGS-TomatoPig11GutMicrobiome-Day14 : num [1:1085] 12 973 0 79 542 16 186 0 185 82 ...
##  $ ShotgunWGS-TomatoPig20GutMicrobiome-Day0  : num [1:1085] 0 3682 0 211 2232 ...
##  $ ShotgunWGS-ControlPig9GutMicrobiome-Day0  : num [1:1085] 2 2717 1 160 1547 ...
##  $ ShotgunWGS-TomatoPig11GutMicrobiome-Day7  : num [1:1085] 0 375 0 31 227 7 69 0 89 29 ...
##  $ ShotgunWGS-TomatoPig13GutMicrobiome-Day7  : num [1:1085] 0 2158 0 159 1774 ...
##  $ ShotgunWGS-TomatoPig17GutMicrobiome-Day0  : num [1:1085] 0 1409 0 197 762 ...
##  $ ShotgunWGS-TomatoPig19GutMicrobiome-Day14 : num [1:1085] 89 1059 0 81 580 ...
##  $ ShotgunWGS-TomatoPig13GutMicrobiome-Day0  : num [1:1085] 1 3634 0 207 2188 ...
##  $ ShotgunWGS-ControlPig2GutMicrobiome-Day14 : num [1:1085] 106 6111 0 386 3446 ...
##  $ ShotgunWGS-ControlPig1GutMicrobiome-Day7  : num [1:1085] 0 3815 1 190 1775 ...
##  $ ShotgunWGS-TomatoPig15GutMicrobiome-Day7  : num [1:1085] 1 1126 0 67 974 ...
##  $ ShotgunWGS-TomatoPig15GutMicrobiome-Day0  : num [1:1085] 0 3134 0 224 2207 ...
##  $ ShotgunWGS-TomatoPig12GutMicrobiome-Day7  : num [1:1085] 0 2376 0 144 1437 ...
##  $ ShotgunWGS-TomatoPig14GutMicrobiome-Day14 : num [1:1085] 0 1079 0 61 469 ...
##  $ ShotgunWGS-TomatoPig20GutMicrobiome-Day14 : num [1:1085] 47 926 0 61 486 18 205 0 193 41 ...
##  $ ShotgunWGS-ControlPig1GutMicrobiome-Day0  : num [1:1085] 0 5545 0 310 2638 ...
##  $ ShotgunWGS-ControlPig4GutMicrobiome-Day14 : num [1:1085] 102 3677 0 270 1919 ...
##  $ ShotgunWGS-ControlPig6GutMicrobiome-Day0  : num [1:1085] 4 2687 0 200 1640 ...
##  $ ShotgunWGS-TomatoPig16GutMicrobiome-Day0  : num [1:1085] 0 2959 0 176 1599 ...
##  $ ShotgunWGS-TomatoPig16GutMicrobiome-Day14 : num [1:1085] 3 973 0 98 609 34 222 0 255 149 ...
##  $ ShotgunWGS-TomatoPig18GutMicrobiome-Day14 : num [1:1085] 11 1075 0 86 446 ...
##  $ ShotgunWGS-ControlPig7GutMicrobiome-Day7  : num [1:1085] 0 1587 0 103 654 ...
##  $ ShotgunWGS-ControlPig4GutMicrobiome-Day7  : num [1:1085] 0 1709 0 165 1059 ...
##  $ ShotgunWGS-TomatoPig13GutMicrobiome-Day14 : num [1:1085] 0 1021 0 74 685 ...
##  $ ShotgunWGS-ControlPig8GutMicrobiome-Day7  : num [1:1085] 12 3035 0 259 1579 ...
##  $ ShotgunWGS-TomatoPig15GutMicrobiome-Day14 : num [1:1085] 17 1660 0 140 892 35 366 0 410 224 ...
##  $ ShotgunWGS-TomatoPig12GutMicrobiome-Day14 : num [1:1085] 19 2138 0 129 1334 ...
##  $ ShotgunWGS-TomatoPig20GutMicrobiome-Day7  : num [1:1085] 0 1699 0 121 645 ...
##  $ ShotgunWGS-ControlPig1GutMicrobiome-Day14 : num [1:1085] 14 3895 0 338 2158 ...
##  $ ShotgunWGS-ControlPig3GutMicrobiome-Day0  : num [1:1085] 0 4578 0 367 2356 ...
##  $ ShotgunWGS-ControlPig5GutMicrobiome-Day0  : num [1:1085] 0 4842 0 274 2894 ...
##  $ ShotgunWGS-ControlPig4GutMicrobiome-Day0  : num [1:1085] 0 4439 0 261 2733 ...
##  $ ShotgunWGS-ControlPig9GutMicrobiome-Day7  : num [1:1085] 0 703 0 54 384 6 159 0 200 45 ...
##  $ ShotgunWGS-ControlPig3GutMicrobiome-Day7  : num [1:1085] 6 4833 0 347 3158 ...
##  $ ShotgunWGS-TomatoPig17GutMicrobime-Day7   : num [1:1085] 0 1713 0 136 782 ...

2.1 Data filtering

2.1.1 Remove inplausible phyla

These phyla are not plausibly found in a rectal swab of a pig, and were incorrectly annotated, so we are removing them.

Genus.Counts.Filt <- Genus.AllSamples.Counts %>%
  filter(phylum != "Chordata" , phylum != "Arthropoda" , phylum != "Cnidaria" , 
         phylum != "Porifera" , phylum != "Echinodermata", phylum != "Streptophyta",
         phylum != "Platyhelminthes")

Transpose.

Genus.Counts.Filt.t <- as.tibble(t(Genus.Counts.Filt))

## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.

## Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if `.name_repair` is omitted as of tibble 2.0.0.
## Using compatibility `.name_repair`.

# make genus colnames
colnames(Genus.Counts.Filt.t) <- Genus.Counts.Filt.t[6,]

# remove domain, phylum, class, order, family
GenusOnly.Counts.Filt.t <- Genus.Counts.Filt.t[7:66,]

# convert character to numeric
GenusOnly.Counts.Filt.t <- as.data.frame(apply((GenusOnly.Counts.Filt.t), 2, as.numeric))

str(GenusOnly.Counts.Filt.t[,1:5])

## 'data.frame':    60 obs. of  5 variables:
##  $ AHJD-like viruses: num  29 0 153 0 14 1 2 0 0 0 ...
##  $ Abiotrophia      : num  5067 5661 4117 1576 3708 ...
##  $ Acanthamoeba     : num  0 0 0 1 0 0 0 0 0 1 ...
##  $ Acaryochloris    : num  271 416 267 131 230 146 133 141 338 272 ...
##  $ Acetivibrio      : num  1988 2981 2071 1012 1991 ...

# add back sample names as column
GenusOnly.Counts.Filt.t <- GenusOnly.Counts.Filt.t %>%
  mutate(Sample_Name = AllSamples.Metadata$Sample_Name)

# move Sample_Name to first column
GenusOnly.Counts.Filt.t <- GenusOnly.Counts.Filt.t %>%
  relocate(Sample_Name)

kable(head(GenusOnly.Counts.Filt.t))

Sample_Name	AHJD-like viruses	Abiotrophia	Acanthamoeba	Acaryochloris	Acetivibrio	Acetobacter	Acetohalobium	Acholeplasma	Achromobacter	Acidaminococcus	Acidilobus	Acidimicrobium	Acidiphilium	Acidithiobacillus	Acidobacterium	Acidothermus	Acidovorax	Aciduliprofundum	Acinetobacter	Actinobacillus	Actinomyces	Actinosynnema	Aerococcus	Aeromicrobium	Aeromonas	Aeropyrum	Afipia	Aggregatibacter	Agrobacterium	Ahrensia	Ajellomyces	Akkermansia	Albidiferax	Alcanivorax	Algoriphagus	Alicycliphilus	Alicyclobacillus	Aliivibrio	Alistipes	Alkalilimnicola	Alkaliphilus	Allochromatium	Allomyces	Alphabaculovirus	Alphatorquevirus	Alteromonas	Aminobacterium	Aminomonas	Ammonifex	Amycolatopsis	Anabaena	Anaerobaculum	Anaerococcus	Anaerofustis	Anaeromyxobacter	Anaerostipes	Anaerotruncus	Anaplasma	Anoxybacillus	Aquifex	Arcanobacterium	Archaeoglobus	Arcobacter	Aromatoleum	Arthrobacter	Arthroderma	Arthrospira	Ascovirus	Asfivirus	Aspergillus	Asticcacaulis	Atopobium	Aurantimonas	Aureococcus	Avibacterium	Avipoxvirus	Azoarcus	Azorhizobium	Azospirillum	Azotobacter	Babesia	Bacillus	Bacteroides	Bartonella	Basfia	Basidiobolus	Batrachochytrium	Bdellomicrovirus	Bdellovibrio	Beggiatoa	Beijerinckia	Bermanella	Betabaculovirus	Betaentomopoxvirus	Betaretrovirus	Beutenbergia	Bicaudavirus	Bifidobacterium	Bigelowiella	Blastocystis	Blastopirellula	Blattabacterium	Blautia	Bocavirus	Boothiomyces	Bordetella	Borrelia	Botryotinia	Bpp-1-like viruses	Brachybacterium	Brachyspira	Bradyrhizobium	Brevibacillus	Brevibacterium	Brevundimonas	Brucella	Brugia	Bryopsis	Buchnera	Bulleidia	Burkholderia	Butyrivibrio	Caenorhabditis	Cafeteria	Caldanaerobacter	Caldicellulosiruptor	Calditerrivibrio	Caldivirga	Caminibacter	Campylobacter	Candida	Candidatus Accumulibacter	Candidatus Amoebophilus	Candidatus Azobacteroides	Candidatus Blochmannia	Candidatus Carsonella	Candidatus Cloacamonas	Candidatus Desulforudis	Candidatus Hamiltonella	Candidatus Hodgkinia	Candidatus Korarchaeum	Candidatus Koribacter	Candidatus Liberibacter	Candidatus Pelagibacter	Candidatus Phytoplasma	Candidatus Protochlamydia	Candidatus Puniceispirillum	Candidatus Regiella	Candidatus Riesia	Candidatus Solibacter	Candidatus Sulcia	Candidatus Zinderia	Capnocytophaga	Carboxydothermus	Cardiobacterium	Carnobacterium	Catenibacterium	Catenulispora	Catonella	Caulobacter	Cavemovirus	Cellulomonas	Cellulosilyticum	Cellvibrio	Cenarchaeum	Chaetomium	Chelativorans	Chitinophaga	Chlamydia	Chlamydiamicrovirus	Chlamydomonas	Chlamydophila	Chlorella	Chloriridovirus	Chlorobaculum	Chlorobium	Chloroflexus	Chloroherpeton	Chlorovirus	Chondrus	Chromera	Chromobacterium	Chromohalobacter	Chryseobacterium	Chrysodidymus	Chthoniobacter	Citreicella	Citrobacter	Citromicrobium	Cladochytrium	Clavibacter	Clavispora	Clostridium	Coccidioides	Coccolithovirus	Coelomomyces	Collimonas	Collinsella	Colwellia	Comamonas	Conexibacter	Congregibacter	Conidiobolus	Coprinopsis	Coprobacillus	Coprococcus	Coprothermobacter	Coraliomargarita	Corynebacterium	Coxiella	Crinivirus	Croceibacter	Crocosphaera	Cronobacter	Cryptobacterium	Cryptomonas	Cryptosporidium	Cupriavidus	Cyanidioschyzon	Cyanidium	Cyanobium	Cyanophora	Cyanothece	Cylindrospermopsis	Cylindrospermum	Cyprinivirus	Cytomegalovirus	Cytophaga	Debaryomyces	Dechloromonas	Deferribacter	Dehalococcoides	Dehalogenimonas	Deinococcus	Delftia	Denitrovibrio	Dependovirus	Dermacoccus	Desulfarculus	Desulfatibacillum	Desulfitobacterium	Desulfobacterium	Desulfococcus	Desulfohalobium	Desulfomicrobium	Desulfonatronospira	Desulfotalea	Desulfotomaculum	Desulfovibrio	Desulfurispirillum	Desulfurivibrio	Desulfurococcus	Desulfuromonas	Dethiobacter	Dethiosulfovibrio	Dialister	Dichelobacter	Dickeya	Dictyoglomus	Dictyostelium	Dinoroseobacter	Dokdonia	Dorea	Durinskia	Dyadobacter	Ectocarpus	Edwardsiella	Eggerthella	Ehrlichia	Eikenella	Eimeria	Elusimicrobium	Emericella	Emiliania	Encephalitozoon	Endoriftia	Enhydrobacter	Entamoeba	Enterobacter	Enterococcus	Enterocytozoon	Entomophthora	Epsilon15-like viruses	Epulopiscium	Eremococcus	Eremothecium	Erwinia	Erysipelothrix	Erythrobacter	Escherichia	Ethanoligenens	Eubacterium	Euglena	Exiguobacterium	Faecalibacterium	Ferrimonas	Ferroglobus	Ferroplasma	Fervidobacterium	Fibrobacter	Filifactor	Filobasidiella	Finegoldia	Flavobacterium	Floydiella	Fluoribacter	Francisella	Frankia	Fucus	Fulvimarina	Fusobacterium	Gallionella	Gammaretrovirus	Gardnerella	Gemella	Gemmata	Gemmatimonas	Geobacillus	Geobacter	Geodermatophilus	Giardia	Gibberella	Glaciecola	Gloeobacter	Gluconacetobacter	Gluconobacter	Gordonia	Gracilaria	Gracilariopsis	Gramella	Granulibacter	Granulicatella	Guillardia	Haemophilus	Hafnia	Hahella	Halalkalicoccus	Halanaerobium	Haliangium	Haloarcula	Halobacterium	Haloferax	Halogeometricum	Halomicrobium	Halomonas	Haloquadratum	Halorhabdus	Halorhodospira	Halorubrum	Haloterrigena	Halothermothrix	Halothiobacillus	Harpochytrium	Helicobacter	Helicosporidium	Heliobacterium	Hemiselmis	Herbaspirillum	Herminiimonas	Herpetosiphon	Heterosigma	Hirschia	Histophilus	Hoeflea	Holdemania	Hyaloraphidium	Hydrogenivirga	Hydrogenobacter	Hydrogenobaculum	Hyperthermus	Hyphomicrobium	Hyphomonas	Hypocrea	Ichnovirus	Idiomarina	Ignicoccus	Ignisphaera	Ilyobacter	Inovirus	Intrasporangium	Iridovirus	Janibacter	Jannaschia	Janthinobacterium	Jonesia	Jonquetella	Kangiella	Ketogulonicigenium	Kineococcus	Kingella	Klebsiella	Kluyveromyces	Kocuria	Kordia	Kosmotoga	Kribbella	Kryptoperidinium	Ktedonobacter	Kytococcus	L5-like viruses	LUZ24-like viruses	Labrenzia	Laccaria	Lachancea	Lachnum	Lactobacillus	Lactococcus	Lambda-like viruses	Laminaria	Laribacter	Lawsonia	Leadbetterella	Leeuwenhoekiella	Legionella	Leifsonia	Leishmania	Lentisphaera	Leotia	Leptosira	Leptospira	Leptospirillum	Leptothrix	Leptotrichia	Leuconostoc	Limnobacter	Listeria	Listonella	Loa	Lodderomyces	Loktanella	Lutiella	Lymphocryptovirus	Lymphocystivirus	Lyngbya	Lysinibacillus	Macavirus	Macrococcus	Magnaporthe	Magnetococcus	Magnetospirillum	Malassezia	Malawimonas	Mannheimia	Maribacter	Maricaulis	Marinobacter	Marinococcus	Marinomonas	Mariprofundus	Maritimibacter	Marivirga	Megasphaera	Meiothermus	Mesoplasma	Mesorhizobium	Metallosphaera	Methanobrevibacter	Methanocaldococcus	Methanocella	Methanococcoides	Methanococcus	Methanocorpusculum	Methanoculleus	Methanohalobium	Methanohalophilus	Methanoplanus	Methanopyrus	Methanoregula	Methanosaeta	Methanosarcina	Methanosphaera	Methanosphaerula	Methanospirillum	Methanothermobacter	Methanothermococcus	Methanothermus	Methylacidiphilum	Methylibium	Methylobacillus	Methylobacter	Methylobacterium	Methylocella	Methylococcus	Methylophaga	Methylosinus	Methylotenera	Methylovorus	Meyerozyma	Micrococcus	Microcoleus	Microcystis	Micromonas	Micromonospora	Microscilla	Mitsuokella	Mobiluncus	Molluscipoxvirus	Moniliophthora	Monomastix	Monosiga	Moorella	Moraxella	Moritella	Mu-like viruses	Mucilaginibacter	Mycobacterium	Mycoplasma	Myxococcus	N15-like viruses	N4-like viruses	Naegleria	Nakamurella	Nakaseomyces	Nanoarchaeum	Natranaerobius	Natrialba	Natronomonas	Nautilia	Nectria	Neisseria	Neolecta	Neorickettsia	Neosartorya	Nephroselmis	Neptuniibacter	Neurospora	Nitratiruptor	Nitrobacter	Nitrococcus	Nitrosococcus	Nitrosomonas	Nitrosopumilus	Nitrosospira	Nitrospira	Nocardia	Nocardioides	Nocardiopsis	Nodularia	Nosema	Nostoc	Novosphingobium	Oceanibulbus	Oceanicaulis	Oceanicola	Oceanithermus	Oceanobacillus	Ochrobactrum	Ochromonas	Octadecabacter	Odontella	Oedogonium	Oenococcus	Oligotropha	Olsenella	Oltmannsiellopsis	Opitutus	Oribacterium	Orientia	Ornithobacterium	Orthopoxvirus	Oscillatoria	Oscillochloris	Ostreavirus	Ostreococcus	Oxalobacter	P1-like viruses	P2-like viruses	P22-like viruses	Paenibacillus	Paludibacter	Pantoea	Parabacteroides	Parachlamydia	Parachlorella	Paracoccidioides	Paracoccus	Paraglomus	Paramecium	Parascardovia	Parvibaculum	Parvularcula	Pasteurella	Paulinella	Paxillus	Pectobacterium	Pediococcus	Pedobacter	Pelagibaca	Pelobacter	Pelodictyon	Pelotomaculum	Penicillium	Peptoniphilus	Peptostreptococcus	Perkinsus	Persephonella	Petrotoga	Phaeobacter	Phaeodactylum	Phaeosphaeria	Phaeovirus	Phenylobacterium	Phi29-like viruses	PhiC31-like viruses	Phieco32-like viruses	Photobacterium	Photorhabdus	Physoderma	Phytophthora	Pichia	Picrophilus	Pirellula	Planctomyces	Planococcus	Plasmodium	Plectrovirus	Plesiocystis	Podospora	Polaribacter	Polaromonas	Polychytrium	Polynucleobacter	Polysphondylium	Porphyra	Porphyromonas	Postia	Prasinovirus	Prevotella	Prochlorococcus	Propionibacterium	Prosthecochloris	Proteromonas	Proteus	Prototheca	Providencia	Pseudendoclonium	Pseudoalteromonas	Pseudomonas	Pseudoramibacter	Pseudovibrio	Psychrobacter	Psychroflexus	Psychromonas	Pycnococcus	Pylaiella	Pyramidobacter	Pyramimonas	Pyrenophora	Pyrobaculum	Pyrococcus	Pythium	Ralstonia	Ranavirus	Raphidiopsis	Reclinomonas	Reinekea	Renibacterium	Rhadinovirus	Rhizobium	Rhodobacter	Rhodococcus	Rhodomicrobium	Rhodomonas	Rhodopirellula	Rhodopseudomonas	Rhodospirillum	Rhodothermus	Rhopalomyces	Rickettsia	Rickettsiella	Riemerella	Robiginitalea	Roseburia	Roseibium	Roseiflexus	Roseobacter	Roseomonas	Roseovarius	Rothia	Rozella	Rubrobacter	Rudivirus	Ruegeria	Ruminococcus	SP6-like viruses	SPO1-like viruses	SPbeta-like viruses	Saccharomonospora	Saccharomyces	Saccharophagus	Saccharopolyspora	Saccoglossus	Sagittula	Salinibacter	Salinispora	Salmonella	Sanguibacter	Saprolegnia	Scardovia	Scenedesmus	Scheffersomyces	Schizophyllum	Schizosaccharomyces	Sclerotinia	Sebaldella	Segniliparus	Selenomonas	Serratia	Shewanella	Shigella	Shuttleworthia	Sideroxydans	Simonsiella	Simplexvirus	Sinorhizobium	Slackia	Sodalis	Sorangium	Sphaerobacter	Sphingobacterium	Sphingobium	Sphingomonas	Sphingopyxis	Spirochaeta	Spiromicrovirus	Spiroplasma	Spirosoma	Sporosarcina	Stackebrandtia	Staphylococcus	Staphylothermus	Starkeya	Stenotrophomonas	Stigeoclonium	Stigmatella	Streptobacillus	Streptococcus	Streptomyces	Streptosporangium	Subdoligranulum	Sulfitobacter	Sulfolobus	Sulfuricurvum	Sulfurihydrogenibium	Sulfurimonas	Sulfurospirillum	Sulfurovum	Symbiobacterium	Synchytrium	Synechococcus	Synechocystis	Synedra	Syntrophobacter	Syntrophomonas	Syntrophothermus	Syntrophus	T1-like viruses	T4-like viruses	T5-like viruses	T7-like viruses	Talaromyces	Tectivirus	Teredinibacter	Terriglobus	Tetragenococcus	Tetrahymena	Thalassiosira	Thalassobium	Thauera	Theileria	Thermaerobacter	Thermanaerovibrio	Thermincola	Thermoanaerobacter	Thermoanaerobacterium	Thermobaculum	Thermobifida	Thermobispora	Thermococcus	Thermocrinis	Thermodesulfovibrio	Thermofilum	Thermomicrobium	Thermomonospora	Thermoplasma	Thermoproteus	Thermosediminibacter	Thermosinus	Thermosipho	Thermosphaera	Thermosynechococcus	Thermotoga	Thermus	Thioalkalivibrio	Thiobacillus	Thiomicrospira	Thiomonas	Tolumonas	Toxoplasma	Treponema	Trichodesmium	Trichomonas	Trichophyton	Trichoplax	Tropheryma	Truepera	Trypanosoma	Tsukamurella	Tuber	Turicibacter	Uncinocarpus	Ureaplasma	Ustilago	VP2-like phages	Vanderwaltozyma	Varicellovirus	Variovorax	Vaucheria	Veillonella	Verminephrobacter	Verrucomicrobium	Verticillium	Vibrio	Victivallis	Volvox	Vulcanisaeta	Waddlia	Weissella	Whispovirus	Wigglesworthia	Wolbachia	Wolinella	Xanthobacter	Xanthomonas	Xenorhabdus	Xylanimonas	Xylella	Yarrowia	Yatapoxvirus	Yersinia	Zunongwangia	Zygosaccharomyces	Zymomonas	c2-like viruses	phiKMV-like viruses	phiKZ-like viruses	unclassified (derived from Actinobacteria (class))	unclassified (derived from Alicyclobacillaceae)	unclassified (derived from Alloherpesviridae)	unclassified (derived from Alphaproteobacteria)	unclassified (derived from Alteromonadales)	unclassified (derived from Bacteria)	unclassified (derived from Bacteroidetes)	unclassified (derived from Betaproteobacteria)	unclassified (derived from Burkholderiales)	unclassified (derived from Campylobacterales)	unclassified (derived from Candidatus Poribacteria)	unclassified (derived from Caudovirales)	unclassified (derived from Chromerida)	unclassified (derived from Chroococcales)	unclassified (derived from Clostridiales Family XI. Incertae Sedis)	unclassified (derived from Clostridiales)	unclassified (derived from Deltaproteobacteria)	unclassified (derived from Elusimicrobia)	unclassified (derived from Erysipelotrichaceae)	unclassified (derived from Euryarchaeota)	unclassified (derived from Flavobacteria)	unclassified (derived from Flavobacteriaceae)	unclassified (derived from Flavobacteriales)	unclassified (derived from Fuselloviridae)	unclassified (derived from Gammaproteobacteria)	unclassified (derived from Lachnospiraceae)	unclassified (derived from Marseillevirus family)	unclassified (derived from Methylophilales)	unclassified (derived from Mononegavirales)	unclassified (derived from Myoviridae)	unclassified (derived from Opitutaceae)	unclassified (derived from Pelagophyceae)	unclassified (derived from Phycodnaviridae)	unclassified (derived from Podoviridae)	unclassified (derived from Poxviridae)	unclassified (derived from Proteobacteria)	unclassified (derived from Rhodobacteraceae)	unclassified (derived from Rhodobacterales)	unclassified (derived from Rickettsiales)	unclassified (derived from Ruminococcaceae)	unclassified (derived from Siphoviridae)	unclassified (derived from Thermotogales)	unclassified (derived from Thiotrichales)	unclassified (derived from Verrucomicrobia subdivision 3)	unclassified (derived from Verrucomicrobiales)	unclassified (derived from Vibrionaceae)	unclassified (derived from Vibrionales)	unclassified (derived from Viruses)	unclassified (derived from other sequences)
ShotgunWGS-ControlPig6GutMicrobiome-Day14	29	5067	0	271	1988	66	1036	779	192	50181	10	59	244	245	365	389	790	217	794	2621	1004	193	341	25	851	31	23	368	517	16	11	1474	342	255	171	81	1438	520	1983	413	13144	198	0	0	8	148	509	101	915	161	473	130	4600	1549	1311	4289	6643	22	1391	554	531	461	513	412	951	14	92	0	0	167	287	35624	112	0	0	0	286	190	165	192	8	27258	318931	222	744	0	0	1	334	81	96	27	0	0	0	406	0	40993	0	0	295	52	17707	0	0	947	291	19	46	301	5181	712	1770	279	126	276	35	1	86	2321	2463	40314	135	0	6815	8872	295	28	33	4599	42	203	196	702	51	0	63	1459	48	0	75	671	23	61	169	177	46	18	7	1612	14	0	2284	4439	72	232	4894	250	177	815	0	291	2656	581	15	30	274	2203	137	1	127	135	0	1	662	2359	907	468	4	0	0	402	350	328	0	85	13	456	16	0	408	17	297762	12	0	0	0	19115	257	119	324	143	0	26	1405	24605	336	203	6812	194	0	475	245	371	3943	1	26	847	8	25	12	5	1558	55	0	0	0	1635	32	358	555	3574	330	1030	221	644	0	31	309	591	14579	526	613	264	575	70	755	11294	4843	331	226	17	644	426	992	24138	330	453	1217	175	136	366	20388	0	1864	0	296	10647	64	28	0	386	54	0	2	13	32	99	615	10711	47	0	4	540	116	36	282	287	333	1977	17513	276925	3	2132	134155	132	126	95	785	4768	594	73	2960	4955	0	0	419	950	0	40	7250	131	1	2547	156	92	211	7035	5443	207	24	96	1	470	112	247	125	2	0	1415	162	478	28	1195	0	433	43	915	283	87	64	52	61	39	121	59	58	226	41	76	2226	98	0	2189	0	5259	1	147	222	519	4	122	474	31	13306	0	69	142	263	43	78	167	0	2	288	43	26	2406	0	149	0	111	128	225	334	271	109	68	359	34	785	38	261	115	371	216	0	140	162	1	0	61	13	29	0	178641	2714	65	0	145	335	1469	1324	556	216	85	102	0	2	516	1	217	2436	1674	31	4368	0	8	13	42	81	0	0	72	1157	0	503	58	579	640	26	0	381	784	137	482	0	565	47	88	536	39571	549	194	308	43	1912	417	179	400	1200	1296	383	128	136	276	105	284	269	1565	499	213	423	350	10	45	102	181	204	40	588	198	374	13	24	141	173	24	220	73	344	48	219	309	106534	5991	0	15	0	57	4305	92	50	0	657	1711	1430	576	0	2	32	220	71	4	1577	32	62	158	24	899	0	55	155	1	38	96	391	324	121	471	423	41	256	191	365	426	178	69	0	689	271	21	98	114	158	1728	274	0	53	8	1	776	144	25686	0	955	19432	45	0	0	42	79	0	92	514	0	1	0	7823	4818	526	24663	32	0	4	337	0	42	846	257	85	410	5	0	694	1736	3321	25	2655	1255	4478	50	1637	1371	32	222	677	20	36	39	0	188	0	0	0	797	335	0	63	8	57	158	375	1	138	0	118	17	901	425	0	192	0	21	8873	5	1	1029670	660	872	145	0	331	0	118	0	769	2976	5626	50	505	190	502	0	0	1711	0	18	97	443	0	581	0	25	6	203	141	0	814	604	739	83	4	603	937	533	648	0	215	9	491	732	93165	17	1503	364	38	136	332	0	817	0	298	145083	5	35	11	133	54	684	421	26	30	456	399	771	550	1	639	0	49	34	57	14	2577	34	36152	519	2720	237	5045	211	50	1	550	13349	101	434	540	443	82	290	263	2362	2	0	2579	0	178	4604	30	98	247	0	128	681	110465	1855	255	82351	48	158	140	608	359	274	232	3519	0	2287	497	0	833	3600	1217	980	0	22	0	0	46	0	172	226	11	34	61	21	165	16	946	739	2853	6464	3550	554	444	223	490	95	359	85	250	198	182	6	1924	2471	905	14	463	2161	570	311	295	238	99	350	43	3169	408	458	1	68	57	174	46	140	8	774	9	192	48	0	11	0	183	1	23909	272	65	7	2167	663	98	17	71	174	0	10	114	430	237	760	138	674	247	52	0	1199	1535	9	199	2	0	4	103	1173	0	56	32	580	946	11	365	48	26	64	0	33	421	12636	186	246	17626	340	602	668	261	0	409	16441	0	11	0	206	86	2	0	7	0	3	39	39	16	15985	768	115	0	120	119	56	33	252	48
ShotgunWGS-ControlPig8GutMicrobiome-Day0	0	5661	0	416	2981	86	1373	1269	298	39909	20	126	344	375	539	554	1502	498	1045	2461	1433	281	354	68	1124	33	21	416	811	45	12	2182	633	333	624	149	1835	704	5891	337	19539	220	0	1	18	287	1036	186	1199	221	741	249	6175	2355	1943	5468	13087	48	1884	674	974	540	708	525	1501	18	126	1	0	145	327	42713	149	2	0	0	470	250	295	300	14	36494	442199	338	862	0	0	4	444	114	164	58	1	0	0	601	1	95976	1	5	514	166	23637	1	0	1069	509	40	14	427	6708	1036	2244	417	171	394	47	0	96	2327	3547	39907	204	0	9162	11870	450	53	66	8514	74	256	377	1730	69	2	160	1952	57	0	97	899	18	79	254	284	70	24	1	1865	49	0	3838	5382	89	338	7907	369	272	953	0	413	4072	586	26	17	457	3454	128	18	150	218	6	0	1106	2794	1107	649	14	0	0	599	500	680	1	375	47	688	32	0	616	25	424056	55	0	0	1	20485	391	250	412	169	0	43	2230	26769	543	473	3838	228	0	877	240	470	5494	1	74	1257	9	18	383	14	2011	56	1	1	0	2596	35	927	588	3698	379	1387	349	956	0	56	436	981	20232	744	911	396	762	124	1047	13009	9335	416	395	35	773	699	1632	10396	320	569	1554	375	193	776	62948	0	2525	0	395	12983	103	56	0	678	62	4	11	42	67	1267	763	15015	76	0	9	897	230	42	375	436	451	4404	24857	252845	0	2864	124365	205	108	114	968	6963	1080	117	3329	7259	0	1	750	1431	0	39	10721	196	1	5115	243	179	340	8831	7012	275	59	174	2	676	221	274	247	3	0	2455	225	617	31	1566	0	567	61	1200	387	114	90	60	96	66	172	109	90	308	50	94	2722	156	0	3541	0	5937	0	181	349	658	1	117	726	45	18363	0	96	169	325	46	133	255	0	0	486	60	24	2995	1	149	3	223	202	371	475	374	162	121	625	69	936	72	291	302	644	346	0	242	240	0	1	127	42	33	0	153576	2761	104	0	193	595	1719	1775	680	404	128	272	0	1	809	1	418	3048	1911	74	5766	0	14	25	80	108	0	0	113	1562	0	601	91	773	826	35	0	387	1442	258	703	0	736	85	132	1023	25118	864	266	523	57	2802	696	221	564	1776	1748	528	155	232	454	176	350	401	2403	614	222	509	539	27	98	207	473	354	84	872	172	534	49	94	231	160	32	285	142	410	74	331	673	91970	10473	0	23	0	88	5010	111	80	4	992	2469	1700	910	0	15	112	326	81	5	2109	73	99	221	35	1301	0	63	181	0	75	97	503	459	178	765	643	54	349	307	462	851	276	83	1	952	391	30	135	159	243	2209	488	0	69	3	0	1108	102	30551	4	1555	7440	81	0	0	95	143	0	161	741	2	16	19	11386	8899	601	39080	47	0	7	338	0	71	1019	366	141	528	15	0	871	2258	5681	35	3491	1261	5463	62	2644	2008	43	276	995	26	83	54	0	173	8	0	0	1130	481	0	156	23	109	290	666	1	236	0	162	40	1645	1012	0	395	0	23	16492	7	4	820657	1108	1128	263	0	427	1	240	0	1015	3925	2424	74	754	328	705	0	1	3061	0	26	134	626	0	761	0	24	2	349	204	1	1049	795	991	151	3	983	1365	772	972	1	306	10	840	1135	72078	39	2141	567	106	243	397	0	1032	0	532	233438	0	42	11	204	108	842	638	18	53	667	550	1238	699	1	855	5	87	40	164	12	3363	68	33861	680	3781	510	4244	176	92	0	907	17333	168	566	856	1017	188	368	326	4106	7	1	3749	0	274	5633	58	178	421	3	206	1115	32512	2943	509	65446	102	212	206	584	432	343	417	4358	0	4236	637	0	1217	4845	1665	1431	0	85	1	35	27	1	249	414	19	56	98	29	243	19	1323	951	3534	8290	4124	739	580	338	814	130	489	121	388	323	300	7	2772	5000	1238	12	599	3069	812	445	448	358	133	480	41	5331	516	1577	7	68	90	280	121	147	27	1135	23	241	98	0	23	0	323	1	19797	508	303	24	3077	1561	166	30	152	212	0	15	200	646	319	1179	169	796	327	109	0	1352	1959	27	269	3	0	6	209	1414	0	141	44	821	2204	17	815	72	49	171	0	47	520	16819	355	329	28383	550	1064	881	725	0	739	21035	0	16	0	587	597	2	2	33	0	2	85	95	12	51452	1069	191	0	336	254	80	33	311	33
ShotgunWGS-ControlPig3GutMicrobiome-Day14	153	4117	0	267	2071	60	1015	817	197	31994	25	81	243	222	309	301	1126	245	653	1798	698	181	737	18	762	26	12	289	443	13	11	1419	464	302	272	141	1369	486	2917	325	13386	173	0	0	5	195	607	112	836	195	485	147	4189	1512	1216	4240	8660	44	1416	469	484	407	569	384	778	14	89	0	1	111	213	19924	84	3	0	0	338	184	238	153	6	26183	294856	170	561	0	0	0	315	50	96	38	0	2	0	222	0	25359	0	1	285	90	18590	0	0	841	246	21	12	223	4840	659	1647	205	115	225	30	1	76	1531	2412	34373	104	0	6622	9318	278	21	37	4769	55	210	216	790	31	1	87	1519	39	0	52	694	10	80	150	183	65	11	2	1413	31	0	2428	4140	69	270	3721	208	246	804	0	203	2758	492	21	31	309	2284	75	0	73	163	3	1	689	1986	898	610	7	0	0	374	336	337	0	168	31	470	27	0	335	39	299010	18	0	0	0	20465	323	169	293	117	0	14	1264	24714	356	247	1818	145	0	435	188	322	3273	0	29	873	6	16	462	10	1563	47	0	1	0	1595	15	369	381	3124	313	966	246	760	0	28	299	604	14708	498	585	338	528	71	822	10274	5420	271	237	22	589	492	895	7351	244	440	1109	153	118	414	32261	2	1656	2	238	9588	59	36	0	452	39	4	2	14	53	113	478	13222	54	0	4	584	103	33	305	209	309	3066	19759	269071	0	2082	175626	142	80	59	794	4985	621	74	2373	4924	0	1	509	954	0	26	6742	161	2	1795	172	80	241	6468	5172	177	24	104	0	536	137	170	103	5	0	1371	176	656	25	1008	0	369	44	910	274	86	46	37	57	35	103	78	41	202	49	75	2050	107	0	2163	0	5150	0	206	270	522	0	98	571	27	12582	0	88	99	182	35	76	198	0	0	326	35	16	2107	1	96	2	117	143	296	243	211	105	83	270	44	600	35	155	167	372	185	1	125	122	1	1	61	14	16	0	163871	4344	49	0	160	372	1507	1139	484	187	61	154	0	1	531	3	269	2470	2143	61	4387	0	12	17	62	63	0	0	94	1202	0	520	65	531	494	20	0	270	763	151	470	0	463	60	112	542	27391	521	194	311	34	1931	418	146	445	1226	1154	430	103	117	321	121	247	268	1527	380	171	373	343	38	54	138	284	228	61	631	162	376	32	77	136	106	23	177	73	376	56	222	348	57206	1189	0	12	1	55	3974	79	40	0	441	1460	1180	576	0	2	37	192	58	4	1539	51	68	159	21	814	0	32	113	1	36	48	297	355	126	482	406	38	241	212	272	397	158	63	0	701	366	20	68	114	153	1575	220	0	52	7	0	876	74	13557	2	1166	7969	78	0	0	72	96	0	112	582	7	3	1	8001	5183	404	23919	22	0	3	203	0	40	369	258	114	274	30	0	609	1780	3350	21	2774	1015	4192	33	1488	1213	19	219	660	18	49	22	0	181	1	1	0	850	288	0	55	13	83	229	397	2	111	0	98	20	906	780	0	271	0	32	8026	5	1	890023	964	618	169	0	274	2	147	0	709	2616	2232	40	465	169	520	0	0	1543	0	9	119	418	0	576	1	20	2	210	95	0	684	577	650	88	7	579	910	592	631	0	223	7	453	803	83235	24	1635	348	62	126	341	0	697	0	377	131810	5	33	13	112	72	871	333	16	28	464	379	760	391	1	376	0	33	31	62	13	2214	42	37426	513	2671	415	3675	139	113	0	586	11188	122	423	579	515	108	317	249	2292	0	0	2461	0	141	4510	51	99	215	1	136	823	431655	1787	294	75804	76	111	141	500	353	258	266	3115	0	3911	496	0	750	3692	1165	910	0	36	0	20	9	0	153	247	24	32	73	13	162	30	964	633	2661	5809	2962	484	355	177	532	85	348	93	304	218	175	8	1960	3508	917	25	474	2175	514	269	324	250	114	285	14	3013	358	466	0	31	40	167	65	93	19	641	10	152	28	0	9	0	292	0	17258	319	140	13	2111	620	109	17	111	219	0	15	129	368	241	770	129	447	213	42	0	1067	1432	12	246	4	0	7	104	1054	0	81	29	577	818	5	672	41	21	63	0	37	319	11368	207	247	19120	311	604	592	345	0	517	16846	0	16	0	214	163	1	0	19	3	0	63	48	9	21377	1145	134	0	159	138	34	16	182	62
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ShotgunWGS-ControlPig5GutMicrobiome-Day7	14	3708	0	230	1991	64	876	787	277	29680	16	90	205	243	341	304	1189	445	896	1778	623	179	224	47	1684	27	31	383	494	30	19	1214	514	341	302	152	1170	647	3503	270	12270	205	0	0	7	334	632	105	815	168	448	139	3168	1475	1134	3828	8846	43	1142	438	523	438	460	416	735	5	88	0	0	87	125	12791	99	1	0	0	406	184	220	225	6	22326	225359	206	673	0	0	2	294	67	101	64	0	0	0	218	1	66063	0	2	300	96	16872	2	0	830	262	24	6	203	4201	669	1387	214	105	258	36	0	202	1097	2653	28995	112	0	5764	8126	244	30	41	5906	36	308	231	808	57	1	83	1241	57	0	66	569	26	72	175	177	53	27	5	922	31	1	1881	3472	77	197	3799	193	178	546	0	191	2805	333	24	16	244	1743	86	4	107	113	4	0	656	1789	706	438	9	0	0	398	364	297	0	230	31	470	29	0	310	8	272328	31	0	0	0	10233	357	194	297	150	0	19	1189	18751	299	302	1826	152	0	433	150	340	2680	0	42	957	6	16	600	4	1285	41	0	0	0	1338	14	823	372	2490	226	825	329	550	0	35	279	608	13231	422	516	261	445	56	673	8336	7391	273	208	24	478	521	940	12959	244	480	897	154	122	367	32973	1	1235	0	416	7772	62	48	0	450	45	3	4	37	50	220	505	9131	76	0	6	539	130	39	297	223	296	1661	16498	181032	2	1686	114740	270	78	94	626	4040	672	49	2351	3360	0	1	530	890	0	36	6669	182	2	3518	173	102	211	5448	4373	164	36	76	0	372	133	153	115	0	0	1229	144	430	15	1322	0	462	35	747	277	86	82	42	55	39	132	51	80	211	40	69	1732	111	0	1893	1	4174	0	240	301	407	1	106	601	35	10907	0	68	100	146	41	64	188	0	0	475	42	24	1924	0	96	0	135	135	347	225	243	191	74	314	61	633	23	130	154	353	168	1	142	126	2	0	72	19	17	0	549401	1876	81	1	188	455	975	1002	548	173	63	174	0	2	485	0	297	1940	1203	95	3963	0	13	24	51	97	0	0	67	946	0	422	47	422	597	18	0	266	725	127	619	0	566	75	97	488	16036	481	172	330	50	1992	533	211	340	1187	1102	361	95	137	298	118	257	294	1574	339	143	371	397	25	81	130	370	302	73	615	141	399	60	105	196	147	13	120	68	256	57	216	317	50915	1108	0	7	0	56	3533	107	167	0	500	1488	1015	493	0	6	45	159	61	4	1383	55	75	151	18	918	0	33	95	0	90	49	296	316	116	503	465	31	295	193	207	413	162	62	0	613	290	20	76	111	143	1336	219	0	60	4	0	693	91	9197	2	858	4884	34	0	0	73	88	0	112	878	2	3	2	7065	3905	494	22016	30	0	6	226	0	39	694	228	100	545	28	1	690	1178	2743	34	2142	804	3400	28	1528	1306	36	172	604	16	47	30	0	103	3	1	0	1070	491	1	80	12	102	245	435	0	87	0	95	12	804	838	0	363	0	19	7825	6	2	507229	900	659	148	0	430	0	253	1	949	2830	1596	53	459	151	730	0	0	1687	0	9	99	427	1	704	0	21	2	211	114	3	650	672	617	82	3	592	1020	585	579	0	214	29	406	633	47030	34	1358	396	87	159	214	0	588	0	395	129723	4	39	2	112	71	575	351	8	34	379	346	837	346	0	570	1	46	26	59	15	2082	51	20968	575	3220	209	2773	178	68	0	606	9232	174	340	582	541	159	304	232	2377	3	0	1727	0	163	4225	54	110	266	2	139	645	41068	1825	269	43518	57	125	117	408	296	260	286	2779	0	4149	393	1	770	2980	979	907	0	42	1	7	21	0	171	184	6	24	59	19	255	14	840	612	2151	5295	2641	400	326	209	537	86	290	82	250	185	217	11	1748	2666	905	5	334	1910	481	348	324	280	111	922	22	3432	335	467	4	27	59	165	46	104	14	815	13	160	37	0	18	0	322	1	13193	336	182	4	2870	1165	99	22	99	119	0	23	145	296	253	780	193	359	296	52	0	1077	964	17	192	6	0	12	133	989	1	124	113	840	1026	30	600	43	22	102	0	26	369	10621	190	215	13467	387	493	425	349	0	607	15029	0	32	0	173	190	0	1	6	0	3	74	95	10	24441	718	129	0	197	139	122	41	200	13
ShotgunWGS-TomatoPig18GutMicrobiome-Day7	1	1159	0	146	585	33	265	338	195	10604	6	85	139	135	171	261	1161	145	368	634	527	172	100	49	356	12	13	115	267	17	9	717	512	104	187	138	488	184	1636	139	4443	90	0	0	92	74	196	34	225	148	229	53	1402	450	593	1437	2878	27	467	183	244	148	194	183	713	2	37	0	0	79	104	15164	47	0	0	0	197	110	105	84	5	8839	168421	67	214	0	0	0	146	26	73	20	0	0	0	203	0	104397	0	0	176	45	7266	2	0	447	134	17	13	166	2073	462	502	216	102	134	49	0	32	378	1349	10996	58	1	2222	3243	109	14	11	1242	10	129	87	418	19	1	25	429	8	0	25	352	7	43	48	69	27	4	0	773	10	0	1136	1109	20	57	1120	178	58	374	0	201	751	223	11	4	170	1247	39	2	64	51	0	0	333	1052	372	250	16	0	0	173	132	203	0	168	13	459	19	0	397	15	101208	12	0	0	0	15652	149	183	150	67	0	12	389	5587	101	172	1853	73	0	204	70	189	1767	0	10	594	1	6	704	5	614	20	0	0	0	789	11	270	104	1041	230	443	248	232	0	37	106	209	4918	195	238	109	174	33	299	2951	1806	91	115	14	217	103	314	3230	101	198	423	62	80	220	8626	0	796	0	129	4429	30	20	0	155	28	0	1	6	14	31	352	4166	30	0	0	170	41	28	160	64	214	16141	8612	66371	0	677	136627	60	35	31	245	2071	169	40	881	2888	0	0	223	766	0	18	2416	66	5	4843	44	91	208	2135	2027	160	4	70	0	231	73	106	92	1	0	678	134	149	6	431	0	169	14	224	144	54	20	14	20	21	60	26	22	143	14	39	643	68	0	927	0	1360	0	173	142	244	0	40	183	15	4918	0	19	26	71	14	84	99	0	0	122	9	6	638	1	130	0	167	88	197	238	103	54	44	326	9	543	23	114	86	142	175	0	62	107	2	1	45	5	16	0	185934	932	255	0	74	181	612	835	237	191	31	51	0	0	235	2	335	796	670	72	1640	2	27	11	42	30	0	0	29	367	0	131	13	201	269	10	0	122	443	82	224	0	208	23	46	242	11524	250	66	264	20	691	176	70	159	427	485	171	39	37	115	56	122	95	508	171	69	141	147	19	18	72	412	129	50	441	129	222	15	125	72	71	10	89	41	145	26	151	154	8831	1015	0	6	0	24	1204	29	34	0	318	1337	396	327	0	0	28	158	38	1	508	19	14	42	9	317	0	14	57	2	19	17	112	209	59	229	261	15	112	81	177	491	122	23	1	347	238	13	54	83	79	493	138	0	22	0	0	344	62	11037	1	718	1696	13	0	0	39	36	0	52	154	120	2	15	2815	3156	176	15566	15	0	1	111	0	23	886	143	54	114	17	0	246	775	1779	30	906	503	1215	13	452	381	25	75	249	12	36	17	0	71	2	1	0	288	155	0	25	7	39	168	303	2	73	0	70	11	515	947	0	332	0	6	4741	4	0	478207	723	647	60	0	133	0	60	2	334	1457	585	15	177	93	177	0	0	559	0	7	42	179	0	380	0	11	2	73	111	1	364	457	492	57	0	358	537	272	315	0	128	4	183	365	15783	17	734	267	67	92	154	0	361	0	216	47208	1	9	7	82	116	419	362	2	19	197	330	1070	303	0	573	1	24	4	40	8	860	23	2400	247	1106	2014	941	84	64	0	354	4993	46	192	395	264	127	230	169	841	0	0	1487	0	101	3009	23	79	145	0	113	358	10184	1565	216	38373	45	53	43	253	100	70	75	1110	0	3488	206	0	304	1271	297	318	0	37	0	5	4	0	88	81	4	10	28	6	115	3	343	223	622	2078	913	198	302	160	171	31	162	41	141	151	88	4	577	674	312	10	189	714	219	162	151	84	89	130	10	1445	156	186	0	29	38	111	24	83	10	210	3	67	38	0	26	0	309	0	5017	314	151	3	962	237	72	6	48	68	0	11	64	125	154	485	67	328	106	18	0	547	736	12	108	6	0	5	139	314	0	53	10	1208	391	5	119	15	17	41	0	12	126	4323	84	75	3971	141	272	292	164	0	273	5333	0	6	0	105	100	0	1	19	0	2	32	25	8	12458	413	48	0	182	81	12	9	87	59

Calculate relative abundance, and bind back to metadata.

GenusOnly.Counts.Filt.t.wtotal <- GenusOnly.Counts.Filt.t %>%
  mutate(Total.Counts = rowSums(GenusOnly.Counts.Filt.t[,2:ncol(GenusOnly.Counts.Filt.t)]))

dim(GenusOnly.Counts.Filt.t.wtotal)

## [1]  60 897

# create rel abund df
RelAbund.Genus.Filt <- GenusOnly.Counts.Filt.t.wtotal[,2:896]/GenusOnly.Counts.Filt.t.wtotal$Total.Counts

# add back metadata
RelAbund.Genus.Filt <- bind_cols(AllSamples.Metadata, RelAbund.Genus.Filt)

2.1.2 Counting missing data

The goal of these next bits of code are to understand how many missing values we have in our dataset, to set what parameters we will use for filtering.

# how many zeros are in the column AHJD-like viruses?
sum(RelAbund.Genus.Filt$`AHJD-like viruses` == 0)  # this code works

## [1] 31

# remove metadata   
# metadata is all character or factor, so can select only numeric columns
RelAbund.Genus.Filt.nometadata <- RelAbund.Genus.Filt %>%
  select_if(is.numeric)

# create a list with the number of zeros for each genus
counting_zeros <- sapply(RelAbund.Genus.Filt.nometadata, 
                         function(x){ (sum(x==0))})

# plot a histogram to look at missing values
counting_zeros_df <- as.data.frame(counting_zeros)

hist(counting_zeros_df$counting_zeros, 
     breaks = 61,
     main = "Histogram of Genera with Zero Relative Intensity",
     sub = "Starting at No Zeros",
     xlab = "Number of zero relative intensity values",
     ylab = "Frequency")

First column is no missing values, and its so big its hard to see how many missing values we actually have.

# plot a histogram to look, but removing genera that are only missing 1 value
counting_zeros_df_missingval <- counting_zeros_df %>%
  rownames_to_column(var = "rowname") %>%
  filter(counting_zeros > 0) %>%
  column_to_rownames(var = "rowname")

# how many genera have at least one missing value?
dim(counting_zeros_df_missingval)

## [1] 186   1

186 genera have at least one missing value.

# histogram of number of zeros, starting at 1 zero
hist(counting_zeros_df_missingval$counting_zeros, 
     breaks = 60,
     main = "Histogram of Genera with Zero Relative Intensity",
     sub = "Starting at 1 Zero",
     xlab = "Number of zero relative intensity values",
     ylab = "Frequency")

# plot a histogram to look, but removing genera that have 20 or more zeros
counting_zeros_df_missing20ormore <- counting_zeros_df %>%
  rownames_to_column(var = "rowname") %>%
  filter(counting_zeros >= 20) %>%
  column_to_rownames(var = "rowname")

# histogram of number of zeros, starting at 20 zero
hist(counting_zeros_df_missing20ormore$counting_zeros, 
     breaks = 40,
     main = "Histogram of Genera with Zero Relative Intensity",
     sub = "Starting at 20 Zero",
     xlab = "Number of zero relative intensity values",
     ylab = "Frequency")

# how many genera have 20 or more missing value?
dim(counting_zeros_df_missing20ormore)

## [1] 140   1

There are 140 genera that have 20 or more missing values. Because 20 missing values here is 1/3 missing, we decided to use this as our cutoff.

2.1.3 Filter for <33% missingness

Our decided criteria:
Filter out genera from relative abundance table that have > 20 zeros, or more than 33% missing data.

# make a character vector of genera names that have > 20 zeros from the rownames in above table
zeros.20 <- c(rownames(counting_zeros_df_missing20ormore))

# filter using this list
RelAbund.Genus.Filt.zerofilt <- RelAbund.Genus.Filt %>%
  rownames_to_column(var = "rowname") %>%
  select(everything(), -all_of(zeros.20)) %>%
  column_to_rownames(var = "rowname")

RelAbund.Genus.Filt.zerofilt[1:3,1:6]

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	Abiotrophia
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	0.0013057
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	0.0013478
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14	0.0010663

dim(RelAbund.Genus.Filt.zerofilt)

## [1]  60 760

Our final dataset has 755 genera (because there are 5 columns of metadata).

Write final dataset genus rel abund to .csv this way we have it.

write_csv(RelAbund.Genus.Filt.zerofilt,
          file = "Genus_RelAbund_Final_Filtered_WithMetadata.csv")

2.2 Microbiome profile

Wrangling to enable collection of some summary statistics about our microbiome profile, including how many genera belong to different domains, etc.

2.2.1 Wrangling

Grab names of final genera.

# contains inplausible genera removed, but not removed for zeroes
dim(Genus.Counts.Filt)

## [1] 895  66

Genus.Counts.Filt[1:5, 1:10]

domain	phylum	class	order	family	genus	ShotgunWGS-ControlPig6GutMicrobiome-Day14	ShotgunWGS-ControlPig8GutMicrobiome-Day0	ShotgunWGS-ControlPig3GutMicrobiome-Day14	ShotgunWGS-TomatoPig14GutMicrobiome-Day7
Viruses	unclassified (derived from Viruses)	unclassified (derived from Viruses)	Caudovirales	Podoviridae	AHJD-like viruses	29	0	153	0
Bacteria	Firmicutes	Bacilli	Lactobacillales	Aerococcaceae	Abiotrophia	5067	5661	4117	1576
Eukaryota	unclassified (derived from Eukaryota)	unclassified (derived from Eukaryota)	unclassified (derived from Eukaryota)	unclassified (derived from Eukaryota)	Acanthamoeba	0	0	0	1
Bacteria	Cyanobacteria	unclassified (derived from Cyanobacteria)	unclassified (derived from Cyanobacteria)	unclassified (derived from Cyanobacteria)	Acaryochloris	271	416	267	131
Bacteria	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Acetivibrio	1988	2981	2071	1012

# final filtered data
RelAbund.Genus.Filt.zerofilt[1:5, 1:10]

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	Abiotrophia	Acaryochloris	Acetivibrio	Acetobacter	Acetohalobium
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	0.0013057	6.98e-05	0.0005123	1.70e-05	0.0002670
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	0.0013478	9.90e-05	0.0007097	2.05e-05	0.0003269
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14	0.0010663	6.91e-05	0.0005364	1.55e-05	0.0002629
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	14	Tomato	Day 7	Tomato Day 7	0.0013116	1.09e-04	0.0008422	3.41e-05	0.0003321
ShotgunWGS-ControlPig5GutMicrobiome-Day7	5	Control	Day 7	Control Day 7	0.0012072	7.49e-05	0.0006482	2.08e-05	0.0002852

# grab colnames which have all the final genera
final_genera <- colnames(RelAbund.Genus.Filt.zerofilt)

# remove metadata colnames
final_genera <- final_genera[6:760]  

final_genera <- as.data.frame(final_genera)

# create a df with the final genera we want to keep for our analysis
final_genera <- final_genera %>%
  rename(genus = final_genera)

Get back domain and inner_join() with final_genera list

# pull from full dataset the domain and genus columns
Genus.Counts.Filt.Domain.Genera <- Genus.Counts.Filt %>%
  select(domain, genus)

Genus.Counts.Filt.Domain.Genera[1:10,]

domain	genus
Viruses	AHJD-like viruses
Bacteria	Abiotrophia
Eukaryota	Acanthamoeba
Bacteria	Acaryochloris
Bacteria	Acetivibrio
Bacteria	Acetobacter
Bacteria	Acetohalobium
Bacteria	Acholeplasma
Bacteria	Achromobacter
Bacteria	Acidaminococcus

# want to join Genus.Counts.Filt.Domain.Genera with final_genera
final_genera_withdomain <- inner_join(final_genera, Genus.Counts.Filt.Domain.Genera,
                                      by = "genus")

2.2.2 Count genera

final_genera_withdomain %>%
  count()

n
755

final_genera_withdomain %>%
  group_by(domain) %>%
  count()

domain	n
Archaea	60
Bacteria	582
Eukaryota	89
other sequences	1
Viruses	23

We have 755 total genera. We have 60 genera from Archaea, 582 from Bacteria, 89 from Eukaryoya, and 23 from Viruses.

2.2.3 Most prevalent genera

What are the most prevalent genera in our pigs?

RelAbund.Genus.Filt.zerofilt[1:5, 1:10]

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	Abiotrophia	Acaryochloris	Acetivibrio	Acetobacter	Acetohalobium
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	0.0013057	6.98e-05	0.0005123	1.70e-05	0.0002670
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	0.0013478	9.90e-05	0.0007097	2.05e-05	0.0003269
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14	0.0010663	6.91e-05	0.0005364	1.55e-05	0.0002629
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	14	Tomato	Day 7	Tomato Day 7	0.0013116	1.09e-04	0.0008422	3.41e-05	0.0003321
ShotgunWGS-ControlPig5GutMicrobiome-Day7	5	Control	Day 7	Control Day 7	0.0012072	7.49e-05	0.0006482	2.08e-05	0.0002852

genera_means <- RelAbund.Genus.Filt.zerofilt %>%
  summarize_if(is.numeric, mean)

genera_means_t <- t(genera_means)
genera_means_t <- as.data.frame(genera_means_t)

genera_means_t <- genera_means_t %>%
  rename(rel_abund_genera = V1) %>%
  arrange(-rel_abund_genera)

head(genera_means_t)

	rel_abund_genera
Prevotella	0.2223133
Bacteroides	0.1034789
Clostridium	0.0855611
Lactobacillus	0.0677779
Eubacterium	0.0516457
Faecalibacterium	0.0448004

The most prevalent genera are Prevotella (22.23% average abundance), Bacteroides (10.34%), Clostridium (8.56%), Lactobacillus (6.78%) and Eubacterium (5.16%).

2.3 Rarefaction curves

2.3.1 Create tax and OTU tables

This section uses a different package than the rest of the analysis; data and metadata need to be uploaded again and made into format friendly for package.

# tax table
TAX_tab <- Genus.AllSamples.Counts %>% 
  select(Domain = domain, Phylum = phylum,
         Class = class, Order = order,
         Family = family, Genus = genus)

tax_names <- colnames(TAX_tab)

head(TAX_tab)

Domain	Phylum	Class	Order	Family	Genus
Viruses	unclassified (derived from Viruses)	unclassified (derived from Viruses)	Caudovirales	Podoviridae	AHJD-like viruses
Bacteria	Firmicutes	Bacilli	Lactobacillales	Aerococcaceae	Abiotrophia
Eukaryota	unclassified (derived from Eukaryota)	unclassified (derived from Eukaryota)	unclassified (derived from Eukaryota)	unclassified (derived from Eukaryota)	Acanthamoeba
Bacteria	Cyanobacteria	unclassified (derived from Cyanobacteria)	unclassified (derived from Cyanobacteria)	unclassified (derived from Cyanobacteria)	Acaryochloris
Bacteria	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Acetivibrio
Bacteria	Proteobacteria	Alphaproteobacteria	Rhodospirillales	Acetobacteraceae	Acetobacter

head(tax_names)

## [1] "Domain" "Phylum" "Class"  "Order"  "Family" "Genus"

#  OTU table
OTU_tab <- Genus.AllSamples.Counts[, seq(7, 66)]

head(OTU_tab)

ShotgunWGS-ControlPig6GutMicrobiome-Day14	ShotgunWGS-ControlPig8GutMicrobiome-Day0	ShotgunWGS-ControlPig3GutMicrobiome-Day14	ShotgunWGS-TomatoPig14GutMicrobiome-Day7	ShotgunWGS-ControlPig5GutMicrobiome-Day7	ShotgunWGS-TomatoPig18GutMicrobiome-Day7	ShotgunWGS-TomatoPig16GutMicrobiome-Day7	ShotgunWGS-ControlPig10GutMicrobiome-Day7	ShotgunWGS-ControlPig2GutMicrobiome-Day0	ShotgunWGS-TomatoPig18GutMicrobiome-Day0	ShotgunWGS-ControlPig10GutMicrobiome-Day0	ShotgunWGS-ControlPig7GutMicrobiome-Day0	ShotgunWGS-ControlPig8GutMicrobiome-Day14	ShotgunWGS-TomatoPig11GutMicrobiome-Day0	ShotgunWGS-TomatoPig19GutMicrobiome-Day0	ShotgunWGS-TomatoPig17GutMicrobiome-Day14	ShotgunWGS-ControlPig9GutMicrobiome-Day14	ShotgunWGS-ControlPig10GutMicrobiome-Day14	ShotgunWGS-TomatoPig19GutMicrobiome-Day7	ShotgunWGS-ControlPig5GutMicrobiome-Day14	ShotgunWGS-ControlPig2GutMicrobiome-Day7	ShotgunWGS-ControlPig6GutMicrobiome-Day7	ShotgunWGS-TomatoPig12GutMicrobiome-Day0	ShotgunWGS-TomatoPig14GutMicrobiome-Day0	ShotgunWGS-ControlPig7GutMicrobiome-Day14	ShotgunWGS-TomatoPig11GutMicrobiome-Day14	ShotgunWGS-TomatoPig20GutMicrobiome-Day0	ShotgunWGS-ControlPig9GutMicrobiome-Day0	ShotgunWGS-TomatoPig11GutMicrobiome-Day7	ShotgunWGS-TomatoPig13GutMicrobiome-Day7	ShotgunWGS-TomatoPig17GutMicrobiome-Day0	ShotgunWGS-TomatoPig19GutMicrobiome-Day14	ShotgunWGS-TomatoPig13GutMicrobiome-Day0	ShotgunWGS-ControlPig2GutMicrobiome-Day14	ShotgunWGS-ControlPig1GutMicrobiome-Day7	ShotgunWGS-TomatoPig15GutMicrobiome-Day7	ShotgunWGS-TomatoPig15GutMicrobiome-Day0	ShotgunWGS-TomatoPig12GutMicrobiome-Day7	ShotgunWGS-TomatoPig14GutMicrobiome-Day14	ShotgunWGS-TomatoPig20GutMicrobiome-Day14	ShotgunWGS-ControlPig1GutMicrobiome-Day0	ShotgunWGS-ControlPig4GutMicrobiome-Day14	ShotgunWGS-ControlPig6GutMicrobiome-Day0	ShotgunWGS-TomatoPig16GutMicrobiome-Day0	ShotgunWGS-TomatoPig16GutMicrobiome-Day14	ShotgunWGS-TomatoPig18GutMicrobiome-Day14	ShotgunWGS-ControlPig7GutMicrobiome-Day7	ShotgunWGS-ControlPig4GutMicrobiome-Day7	ShotgunWGS-TomatoPig13GutMicrobiome-Day14	ShotgunWGS-ControlPig8GutMicrobiome-Day7	ShotgunWGS-TomatoPig15GutMicrobiome-Day14	ShotgunWGS-TomatoPig12GutMicrobiome-Day14	ShotgunWGS-TomatoPig20GutMicrobiome-Day7	ShotgunWGS-ControlPig1GutMicrobiome-Day14	ShotgunWGS-ControlPig3GutMicrobiome-Day0	ShotgunWGS-ControlPig5GutMicrobiome-Day0	ShotgunWGS-ControlPig4GutMicrobiome-Day0	ShotgunWGS-ControlPig9GutMicrobiome-Day7	ShotgunWGS-ControlPig3GutMicrobiome-Day7	ShotgunWGS-TomatoPig17GutMicrobime-Day7
29	0	153	0	14	1	2	0	0	0	0	0	342	0	0	6	131	86	0	76	1	0	0	1	67	12	0	2	0	0	0	89	1	106	0	1	0	0	0	47	0	102	4	0	3	11	0	0	0	12	17	19	0	14	0	0	0	0	6	0
5067	5661	4117	1576	3708	1159	2495	1636	4534	2964	3197	2513	4231	3101	3274	1424	3337	3383	1849	3864	5590	3120	2599	1453	2906	973	3682	2717	375	2158	1409	1059	3634	6111	3815	1126	3134	2376	1079	926	5545	3677	2687	2959	973	1075	1587	1709	1021	3035	1660	2138	1699	3895	4578	4842	4439	703	4833	1713
0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
271	416	267	131	230	146	133	141	338	272	264	263	274	237	228	83	328	238	120	363	306	201	190	70	248	79	211	160	31	159	197	81	207	386	190	67	224	144	61	61	310	270	200	176	98	86	103	165	74	259	140	129	121	338	367	274	261	54	347	136
1988	2981	2071	1012	1991	585	1538	812	2670	1665	1411	1652	1795	2160	1729	683	1722	1976	940	2395	4493	1273	1451	846	1870	542	2232	1547	227	1774	762	580	2188	3446	1775	974	2207	1437	469	486	2638	1919	1640	1599	609	446	654	1059	685	1579	892	1334	645	2158	2356	2894	2733	384	3158	782
66	86	60	41	64	33	51	43	144	60	43	55	54	136	56	27	105	118	22	79	100	46	59	39	71	16	75	48	7	51	22	23	56	106	47	32	52	42	18	18	59	81	53	41	34	10	22	40	33	57	35	45	22	86	72	132	73	6	74	26

2.3.2 Create metadata

Since metadata is contained in column names, we will parse them from here.

raw_names <- colnames(OTU_tab)
names_table <- data.frame(Raw_names = raw_names)

First, the string will split by the middle hyphen.

names_table <- names_table %>% 
  separate(Raw_names, into = c("Shotgun", "Type", "Day")) %>% 
  select(-Shotgun)

Now, since the character GutMicrobiome is constant over all samples, it will be removed. In the same manner, the character Day will be removed.

names_table <- names_table %>% 
  mutate(Type = str_remove(string = Type, pattern = "GutMicrobiome") ) %>% 
  mutate(Type = str_remove(string = Type, pattern = "GutMicrobime") ) %>% 
  mutate(Day = str_remove(string = Day, pattern = "Day"))
head(names_table, 2)

Type	Day
ControlPig6	14
ControlPig8	0

Since Pig is in the middle of the sample type and the pig number, it will be used as separator character. And the final result is a tidy data.

names_table <- names_table %>% 
  separate(col = Type, into = c("Type", "Pig"), sep = "Pig") %>% 
  mutate(Type = factor(Type), Pig = factor(Pig), Day = as.integer(Day)) %>% 
  select(Type, Day, Pig)
head(names_table)

Type	Day	Pig
Control	14	6
Control	0	8
Control	14	3
Tomato	7	14
Control	7	5
Tomato	7	18

2.3.3 Renaming samples

Now that we have tidy data, it’s better to replace long names with shorter ones. New names will be created as Type_Pig_Day. We are also creating names that distinguish the 6 diet by time point groups.

tmp_names  <- names_table %>%
  mutate(Pig = paste0("P", Pig), Day = paste0("D", Day)) %>% 
  unite("Kronas", Type:Day) %>% unite("Sample", Kronas:Pig, remove = F) %>% 
  select(-Pig)
head(tmp_names)

Sample	Kronas
Control_D14_P6	Control_D14
Control_D0_P8	Control_D0
Control_D14_P3	Control_D14
Tomato_D7_P14	Tomato_D7
Control_D7_P5	Control_D7
Tomato_D7_P18	Tomato_D7

metadata <- bind_cols(names_table, tmp_names)
head(metadata)

Type	Day	Pig	Sample	Kronas
Control	14	6	Control_D14_P6	Control_D14
Control	0	8	Control_D0_P8	Control_D0
Control	14	3	Control_D14_P3	Control_D14
Tomato	7	14	Tomato_D7_P14	Tomato_D7
Control	7	5	Control_D7_P5	Control_D7
Tomato	7	18	Tomato_D7_P18	Tomato_D7

Finally, in order to use this metadata with the OTU table, which is linked by names, the row names of the metadata and the column names in the OTU table must be the same.

rownames(names_table) <-  tmp_names$Sample
colnames(OTU_tab) <-   tmp_names$Sample

2.3.4 Creating a phyloseq object

It’s time create a phyloseq object that will allow us to analyze this data easier.

rownames(metadata) <- metadata$Sample
gut_microbiome_raw <- phyloseq(otu_table(OTU_tab, taxa_are_rows = T),
                               tax_table(TAX_tab),
                               sample_data(metadata))

colnames(tax_table(gut_microbiome_raw) ) <- tax_names

We can see that data at genus level accounts with 1085 taxas in 60 samples. But, we had developed a filtering scheme to remove very low abundance and inconsistently detected taxa, so let’s merge this full list

gut_microbiome_raw

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1085 taxa and 60 samples ]
## sample_data() Sample Data:       [ 60 samples by 5 sample variables ]
## tax_table()   Taxonomy Table:    [ 1085 taxa by 6 taxonomic ranks ]

2.3.5 Filtering taxa

We want to only include the taxa we ended up using in our final analysis. We have already created an df final_genera which contains only the genera used in our final analysis.

final_genera_forphyloseq <- final_genera$genus

# subset to include only final genera
gut_microbiome_clean <- subset_taxa(gut_microbiome_raw, Genus %in% final_genera_forphyloseq)

gut_microbiome_clean

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 755 taxa and 60 samples ]
## sample_data() Sample Data:       [ 60 samples by 5 sample variables ]
## tax_table()   Taxonomy Table:    [ 755 taxa by 6 taxonomic ranks ]

The final phyloseq object has 871 taxonomic levels, in our cases, species since it is the lowest taxonomic levels that the sequences were annotated.

In order to check if we have the 45 phyla, we are gonna to count the unique phyla names in the dataset.

length(unique(tax_table(gut_microbiome_clean)[, "Genus"]))

## [1] 755

We got our 755 genera.

2.3.6 Creating rarefaction curves

plot_rarefaction <- ranacapa::ggrare(gut_microbiome_clean, step = 60000,
                                   color = 'Type',  se = F, plot = F) 

## rarefying sample Control_D14_P6
## rarefying sample Control_D0_P8
## rarefying sample Control_D14_P3
## rarefying sample Tomato_D7_P14
## rarefying sample Control_D7_P5
## rarefying sample Tomato_D7_P18
## rarefying sample Tomato_D7_P16
## rarefying sample Control_D7_P10
## rarefying sample Control_D0_P2
## rarefying sample Tomato_D0_P18
## rarefying sample Control_D0_P10
## rarefying sample Control_D0_P7
## rarefying sample Control_D14_P8
## rarefying sample Tomato_D0_P11
## rarefying sample Tomato_D0_P19
## rarefying sample Tomato_D14_P17
## rarefying sample Control_D14_P9
## rarefying sample Control_D14_P10
## rarefying sample Tomato_D7_P19
## rarefying sample Control_D14_P5
## rarefying sample Control_D7_P2
## rarefying sample Control_D7_P6
## rarefying sample Tomato_D0_P12
## rarefying sample Tomato_D0_P14
## rarefying sample Control_D14_P7
## rarefying sample Tomato_D14_P11
## rarefying sample Tomato_D0_P20
## rarefying sample Control_D0_P9
## rarefying sample Tomato_D7_P11
## rarefying sample Tomato_D7_P13
## rarefying sample Tomato_D0_P17
## rarefying sample Tomato_D14_P19
## rarefying sample Tomato_D0_P13
## rarefying sample Control_D14_P2
## rarefying sample Control_D7_P1
## rarefying sample Tomato_D7_P15
## rarefying sample Tomato_D0_P15
## rarefying sample Tomato_D7_P12
## rarefying sample Tomato_D14_P14
## rarefying sample Tomato_D14_P20
## rarefying sample Control_D0_P1
## rarefying sample Control_D14_P4
## rarefying sample Control_D0_P6
## rarefying sample Tomato_D0_P16
## rarefying sample Tomato_D14_P16
## rarefying sample Tomato_D14_P18
## rarefying sample Control_D7_P7
## rarefying sample Control_D7_P4
## rarefying sample Tomato_D14_P13
## rarefying sample Control_D7_P8
## rarefying sample Tomato_D14_P15
## rarefying sample Tomato_D14_P12
## rarefying sample Tomato_D7_P20
## rarefying sample Control_D14_P1
## rarefying sample Control_D0_P3
## rarefying sample Control_D0_P5
## rarefying sample Control_D0_P4
## rarefying sample Control_D7_P9
## rarefying sample Control_D7_P3
## rarefying sample Tomato_D7_P17

plot_rarefaction <-  plot_rarefaction + theme_test() +
  facet_wrap("Day", scales = "free_x", ncol = 1, 
             labeller = labeller(Day = c( `0` = "Day 0" ,
                                          `7` = "Day 7",
                                          `14`= "Day 14")) ) + 
  labs(color = "Diet",
       title = "Rarefaction curves") +
  scale_color_manual(values = c( "steelblue2", "tomato2"))
  
plot_rarefaction

2.4 Krona plots for exploratory analysis

The psadd package is able to create Krona plots with an phyloseq object. Two Kronas will be created, per sample and per category Day + Type. The Krona plots only include the final filtered taxa we used in our analysis.

# Write kronas per samples
plot_krona(physeq = gut_microbiome, output = "./plots/kronas/per_sample", 
           variable = "Sample")

# Write kronas per category (Sample type + Day) i.e. Tomato_D7
plot_krona(physeq = gut_microbiome, output = "./plots/kronas/per_category/", 
           variable = "Kronas")

2.5 PERMANOVA

Use PERMANOVA to conduct statistical analysis of overall microbial profile differences among groups.

2.5.1 All samples, full model

Test the overall effect of Diet, Time_Point and their interaction of the overall microbiome.

# create factors
factors_time_diet_pig <- RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Diet, Pig)

# create permutations
perm_time_diet_pig <- how(nperm = 9999)
setBlocks(perm_time_diet_pig) <- with(factors_time_diet_pig, Pig)

# run permanova
AllData.Genus.Filt.permanova <- adonis2(RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet*Time_Point,
                                        data = factors_time_diet_pig,
                                        permutations = perm_time_diet_pig,
                                        method = "bray")

AllData.Genus.Filt.permanova

	Df	SumOfSqs	R2	F	Pr(>F)
Diet	1	0.0587899	0.0495428	3.4113532	0.0001
Time_Point	2	0.1661187	0.1399897	4.8196152	0.0001
Diet:Time_Point	2	0.0311261	0.0262302	0.9030629	0.3831
Residual	54	0.9306149	0.7842373	NA	NA
Total	59	1.1866497	1.0000000	NA	NA

• Diet: p = 0.0001, significant
  
• Time_Point: p = 0.0001, significant
  
• Diet*Time_Point: p = 0.3831, non-significant

Comparison when you don’t filter out for missing values

AllData.Genus.Filt.permanova.no0filt <- adonis2(RelAbund.Genus.Filt[,-c(1:5)]~Diet*Time_Point,
                                        data = factors_time_diet_pig,
                                        permutations = perm_time_diet_pig,
                                        method = "bray")
AllData.Genus.Filt.permanova.no0filt

	Df	SumOfSqs	R2	F	Pr(>F)
Diet	1	0.0588014	0.0495426	3.4113887	0.0001
Time_Point	2	0.1661637	0.1399998	4.8200290	0.0001
Diet:Time_Point	2	0.0311336	0.0262314	0.9031152	0.3750
Residual	54	0.9307872	0.7842263	NA	NA
Total	59	1.1868860	1.0000000	NA	NA

• Diet: p = 0.0001, significant
  
• Time_Point: p = 0.0001, significant
  
• Diet*Time_Point: p = 0.3750, non-significant

Significance is the same whether you filter for missing data or not.

2.5.2 Subset by diet

2.5.2.1 Control

Effect of control diet of time.

# filter for only control
control.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Diet == "Control")

# create factors
factors_control_genera <- control.RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Pig)

# create permutations
perm_control_genera <- how(nperm = 9999)
setBlocks(perm_control_genera) <- with(factors_control_genera, Pig)

# run PERMANOVA
Control.ByTime.Genus.zerofilt <- adonis2(control.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                         data = factors_control_genera,
                                         permutations = perm_control_genera, 
                                         method = "bray")
Control.ByTime.Genus.zerofilt

	Df	SumOfSqs	R2	F	Pr(>F)
Time_Point	2	0.1350738	0.225784	3.936994	1e-04
Residual	27	0.4631695	0.774216	NA	NA
Total	29	0.5982432	1.000000	NA	NA

Significant effect of time (p = 0.001) within control samples.

2.5.2.2 Tomato

Effect of tomato diet over time.

# filter data set for only tomato samples 
tomato.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Diet == "Tomato")

# create factors
factors_tomato_genera <- tomato.RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Pig)

# create permutations
perm_tomato_genera <- how(nperm = 9999)
setBlocks(perm_tomato_genera) <- with(factors_tomato_genera, Pig)

# run PERMANOVA
Tomato.ByTime.Genus.zerofilt <- adonis2(tomato.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                        data = factors_tomato_genera,
                                        permutations = perm_tomato_genera, 
                                        method = "bray")
Tomato.ByTime.Genus.zerofilt

	Df	SumOfSqs	R2	F	Pr(>F)
Time_Point	2	0.0621711	0.1173888	1.795523	0.0195
Residual	27	0.4674455	0.8826112	NA	NA
Total	29	0.5296165	1.0000000	NA	NA

Significant effect of time (p = 0.0195) within tomato samples

2.5.3 Subset by time

2.5.3.1 Day 0

Effect of diet at day 0.

# filter for day 0 only
d0.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Time_Point == "Day 0")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day0_genera <- d0.RelAbund.Genus.Filt.zerofilt %>% 
  select(Diet)

# create permutations
perm_day0_genera <- how(nperm = 9999)

# run PERMANOVA
d0.ByTime.Genus.zerofilt <- adonis2(d0.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet,
                                         data = factors_day0_genera,
                                         permutations = perm_day0_genera,
                                         method = "bray")
d0.ByTime.Genus.zerofilt

	Df	SumOfSqs	R2	F	Pr(>F)
Diet	1	0.0201379	0.067603	1.305081	0.2402
Residual	18	0.2777468	0.932397	NA	NA
Total	19	0.2978847	1.000000	NA	NA

Non-significant effect of Diet (p = 0.2402) at day 0.

2.5.3.2 Day 7

Effect of diet at day 7.

# filter for day 7 only
d7.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Time_Point == "Day 7")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day7_genera <- d7.RelAbund.Genus.Filt.zerofilt %>% 
  select(Diet)

# create permutations
perm_day7_genera <- how(nperm = 9999)

# run PERMANOVA
d7.ByTime.Genus.zerofilt <- adonis2(d7.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet,
                                         data = factors_day7_genera,
                                         permutations = perm_day7_genera,
                                         method = "bray")
d7.ByTime.Genus.zerofilt

	Df	SumOfSqs	R2	F	Pr(>F)
Diet	1	0.0278005	0.0637997	1.226655	0.2836
Residual	18	0.4079456	0.9362003	NA	NA
Total	19	0.4357460	1.0000000	NA	NA

Non-significant effect of Diet (p = 0.2836) at day 7.

2.5.3.3 Day 14

Effect of diet at day 14.

# filter for day 14 only
d14.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Time_Point == "Day 14")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day14_genera <- d14.RelAbund.Genus.Filt.zerofilt %>% 
  select(Diet)

# create permutations
perm_day14_genera <- how(nperm = 9999)

# run PERMANOVA
d14.ByTime.Genus.zerofilt <- adonis2(d14.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet,
                                     data = factors_day14_genera,
                                     permutations = perm_day14_genera,
                                     method = "bray")
d14.ByTime.Genus.zerofilt

	Df	SumOfSqs	R2	F	Pr(>F)
Diet	1	0.0419776	0.1463144	3.085046	0.005
Residual	18	0.2449226	0.8536856	NA	NA
Total	19	0.2869002	1.0000000	NA	NA

Significant effect of Diet (p = 0.005) at day 14.

2.6 PCoA Beta Diversity

2.6.0.1 All samples

# calculate distances
genus.filt.dist.20zeros <- vegdist(RelAbund.Genus.Filt.zerofilt[6:ncol(RelAbund.Genus.Filt.zerofilt)], 
                                   method = "bray")

# do multi-dimensional scaling (the PCoA calculations) on those distances
scale.genus.filt.20zeros <- cmdscale(genus.filt.dist.20zeros, k=2)

# make into data frame
scale.genus.filt.df.20zeros <- as.data.frame(cbind(scale.genus.filt.20zeros, 
                                                   AllSamples.Metadata))

# do PCoA again, but get eigen values
scale.genus.filt.20zeros.eig <- cmdscale(genus.filt.dist.20zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
eigs.genus.filt.20zeros <- (100* ((scale.genus.filt.20zeros.eig$eig)/(sum(scale.genus.filt.20zeros.eig$eig))))

# round the converted eigenvalues
round.eigs.genus.20zeros <- round(eigs.genus.filt.20zeros, 3)

All samples, one PCoA

PCoA_genera_20zeros_allsamples <- scale.genus.filt.df.20zeros %>%
ggplot(aes(x = `1`, y = `2`, fill = Diet_By_Time_Point)) +
  geom_point(size=3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black"))+
  labs(x=paste("PC1: ", round.eigs.genus.20zeros[1], "%"), 
       y=paste("PC2: ", round.eigs.genus.20zeros[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Genus Level") 

PCoA_genera_20zeros_allsamples

ggsave("Figures/BetaDiversity_PCoA_Genera_allsamples.png", 
       plot = PCoA_genera_20zeros_allsamples, 
       dpi = 800, 
       width = 10, 
       height = 8)

Re-level factors

scale.genus.filt.df.20zeros <- scale.genus.filt.df.20zeros %>% 
  mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))

2.6.0.1.1 Facet by time point

PCoA_genera_20zeros_facetbytime <- scale.genus.filt.df.20zeros %>%
ggplot(aes(x = `1`, y = `2`, fill = Diet_By_Time_Point)) +
  geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_point(size=3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_bw() +
  theme(axis.text = element_text(color = "black"),
        strip.background =element_rect(fill="white"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  labs(x=paste("PC1: ", round.eigs.genus.20zeros[1], "%"), 
       y=paste("PC2: ", round.eigs.genus.20zeros[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Genera Level, Subset by Time Point") +
  facet_wrap(~Time_Point)

PCoA_genera_20zeros_facetbytime

ggsave("Figures/BetaDiversity_PCoA_Genera_FacetByTimePoint.png", 
       plot = PCoA_genera_20zeros_facetbytime, 
       dpi = 800, 
       width = 10, 
       height = 6)

2.6.0.1.2 Facet by diet

PCoA_genera_20zeros_facetbydiet <- scale.genus.filt.df.20zeros %>%
ggplot(aes(x = `1`, y = `2`, fill = Diet_By_Time_Point)) +
  geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_point(size=3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_bw() +
  theme(axis.text = element_text(color = "black"),
        strip.background =element_rect(fill="white"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  labs(x=paste("PC1: ", round.eigs.genus.20zeros[1], "%"), 
       y=paste("PC2: ", round.eigs.genus.20zeros[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Genera Level, Subset by Diet") +
  facet_wrap(~Diet)

PCoA_genera_20zeros_facetbydiet

ggsave("Figures/BetaDiversity_PCoA_Genera_FacetByDiet.png", 
       plot = PCoA_genera_20zeros_facetbydiet, 
       dpi = 800, 
       width = 10, 
       height = 6)

2.6.1 Subset

Ended up not using this as part of the paper. Since the input is different here (i.e., the PCoA only has the subset data as an input) the output looks slightly different and we didn’t feel this was the most accurate depction of the data.

2.6.1.1 Control only

# calculate distances
control.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(control.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], 
                                                     method = "bray")

# calculate to make PCoA
control.scale.genus.filt.20zeros <- cmdscale(control.RelAbund.Genus.Filt.zerofilt.dist, k=2)

# filter metadata
meta.control <- subset(AllSamples.Metadata, Diet == "Control")

# make into data frame and add metadata
control.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.control, control.scale.genus.filt.20zeros))

# get eigenvalues
control.scale.genus.filt.20zeros.eig <- cmdscale(control.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
control.eigs.genus.filt.20zeros <- (100*((control.scale.genus.filt.20zeros.eig$eig)/(sum(control.scale.genus.filt.20zeros.eig$eig))))
control.round.eigs.genus.20zeros <- round(control.eigs.genus.filt.20zeros, 3)

Reset factor levels

control.scale.genus.filt.20zeros.df$Time_Point <- factor(control.scale.genus.filt.20zeros.df$Time_Point, levels = c("Day 0", "Day 7", "Day 14"))

Plot

control.scale.genus.filt.20zeros.df %>%
ggplot(aes(x = `1`, y = `2`, fill = Time_Point)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4"))+
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=(paste(control.round.eigs.genus.20zeros[1], "%")), 
       y=(paste(control.round.eigs.genus.20zeros[2], "%")), 
       fill = "Time Point",
       title = "Beta Diversity",
       subtitle = "Genera Level, Control Samples Only")

2.6.1.2 Tomato only

# calculate distances
tomato.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(tomato.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")

# calculate to make PCoA
tomato.scale.genus.filt.20zeros <- cmdscale(tomato.RelAbund.Genus.Filt.zerofilt.dist, k=2)

# filter metadata
meta.tomato <- subset(AllSamples.Metadata, Diet == "Tomato")

# make into data frame and add metadata
tomato.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.tomato, tomato.scale.genus.filt.20zeros))

# get eigenvalues
tomato.scale.genus.filt.20zeros.eig <- cmdscale(tomato.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
tomato.eigs.genus.filt.20zeros <- (100*((tomato.scale.genus.filt.20zeros.eig$eig)/(sum(tomato.scale.genus.filt.20zeros.eig$eig))))
tomato.round.eigs.genus.20zeros <- round(tomato.eigs.genus.filt.20zeros, 3)

Reset factor levels

tomato.scale.genus.filt.20zeros.df$Time_Point <- factor(tomato.scale.genus.filt.20zeros.df$Time_Point, levels = c("Day 0", "Day 7", "Day 14"))

Plot

tomato.scale.genus.filt.20zeros.df %>%
ggplot(aes(x = `1`, y = `2`, fill = Time_Point))+
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values = c("sienna1","firebrick3","tomato4"))+
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=(paste(tomato.round.eigs.genus.20zeros[1], "%")), 
       y=(paste(tomato.round.eigs.genus.20zeros[2], "%")), 
       fill = "Time Point",
       title = "Beta Diversity",
       subtitle = "Genera Level, Tomato Samples Only")

2.6.1.3 Day 0 Only

# calculate distances
d0.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(d0.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")

# calculate to make PCoA
d0.scale.genus.filt.20zeros <- cmdscale(d0.RelAbund.Genus.Filt.zerofilt.dist, k=2)

# filter metadata
meta.day0 <- subset(AllSamples.Metadata, Time_Point == "Day 0")

# make into data frame and add metadata
d0.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.day0, d0.scale.genus.filt.20zeros))

# get eigenvalues
d0.scale.genus.filt.20zeros.eig <- cmdscale(d0.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
d0.eigs.genus.filt.20zeros <- (100*((d0.scale.genus.filt.20zeros.eig$eig)/(sum(d0.scale.genus.filt.20zeros.eig$eig))))
d0.round.eigs.genus.20zeros <- round(d0.eigs.genus.filt.20zeros, 3)

Plot

d0.scale.genus.filt.20zeros.df %>%
ggplot(aes( x= `1`, y = `2`, fill = Diet))+
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values = c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=(paste(d0.round.eigs.genus.20zeros[1], "%")), 
       y=(paste(d0.round.eigs.genus.20zeros[2], "%")),
       title = "Beta Diversity",
       subtitle = "Genera Level, Day 0 Only")

2.6.1.4 Day 7 Only

# calculate distances
d7.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(d7.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")

# calculate to make PCoA
d7.scale.genus.filt.20zeros <- cmdscale(d7.RelAbund.Genus.Filt.zerofilt.dist, k=2)

# filter metadata
meta.day7 <- subset(AllSamples.Metadata, Time_Point == "Day 7")

# make into data frame and add metadata
d7.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.day7, d7.scale.genus.filt.20zeros))

# get eigenvalues
d7.scale.genus.filt.20zeros.eig <- cmdscale(d7.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
d7.eigs.genus.filt.20zeros <- (100*((d7.scale.genus.filt.20zeros.eig$eig)/(sum(d7.scale.genus.filt.20zeros.eig$eig))))
d7.round.eigs.genus.20zeros <- round(d7.eigs.genus.filt.20zeros, 3)

Plot

d7.scale.genus.filt.20zeros.df %>%
ggplot(aes( x= `1`, y = `2`, fill = Diet))+
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_color_manual(values = c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=(paste(d7.round.eigs.genus.20zeros[1], "%")), 
       y=(paste(d7.round.eigs.genus.20zeros[2], "%")),
       title = "Beta Diversity",
       subtitle = "Genera Level, Day 7 Only")

2.6.1.5 Day 14 Only

# calculate distances
d14.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(d14.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")
# calculate to make PCoA
d14.scale.genus.filt.20zeros <- cmdscale(d14.RelAbund.Genus.Filt.zerofilt.dist, k=2)

# filter metadata
meta.day14 <- subset(AllSamples.Metadata, Time_Point == "Day 14")

# make into data frame and add metadata
d14.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.day14, d14.scale.genus.filt.20zeros))

# get eigenvalues
d14.scale.genus.filt.20zeros.eig <- cmdscale(d14.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
d14.eigs.genus.filt.20zeros <- (100*((d14.scale.genus.filt.20zeros.eig$eig)/(sum(d14.scale.genus.filt.20zeros.eig$eig))))
d14.round.eigs.genus.20zeros <- round(d14.eigs.genus.filt.20zeros, 3)

Plot

d14.scale.genus.filt.20zeros.df %>%
ggplot(aes( x= `1`, y = `2`, fill = Diet))+
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_color_manual(values = c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=(paste(d14.round.eigs.genus.20zeros[1], "%")), 
       y=(paste(d0.round.eigs.genus.20zeros[2], "%")),
       title = "Beta Diversity",
       subtitle = "Genera Level, Day 14 Only")

2.7 Alpha Diversity

2.7.1 Wrangling

kable(head(RelAbund.Genus.Filt.zerofilt))

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	Abiotrophia	Acaryochloris	Acetivibrio	Acetobacter	Acetohalobium	Acholeplasma	Achromobacter	Acidaminococcus	Acidilobus	Acidimicrobium	Acidiphilium	Acidithiobacillus	Acidobacterium	Acidothermus	Acidovorax	Aciduliprofundum	Acinetobacter	Actinobacillus	Actinomyces	Actinosynnema	Aerococcus	Aeromicrobium	Aeromonas	Aeropyrum	Afipia	Aggregatibacter	Agrobacterium	Ahrensia	Ajellomyces	Akkermansia	Albidiferax	Alcanivorax	Algoriphagus	Alicycliphilus	Alicyclobacillus	Aliivibrio	Alistipes	Alkalilimnicola	Alkaliphilus	Allochromatium	Alphatorquevirus	Alteromonas	Aminobacterium	Aminomonas	Ammonifex	Amycolatopsis	Anabaena	Anaerobaculum	Anaerococcus	Anaerofustis	Anaeromyxobacter	Anaerostipes	Anaerotruncus	Anaplasma	Anoxybacillus	Aquifex	Arcanobacterium	Archaeoglobus	Arcobacter	Aromatoleum	Arthrobacter	Arthroderma	Arthrospira	Aspergillus	Asticcacaulis	Atopobium	Aurantimonas	Azoarcus	Azorhizobium	Azospirillum	Azotobacter	Babesia	Bacillus	Bacteroides	Bartonella	Basfia	Bdellovibrio	Beggiatoa	Beijerinckia	Bermanella	Beutenbergia	Bifidobacterium	Blastopirellula	Blattabacterium	Blautia	Bordetella	Borrelia	Botryotinia	Bpp-1-like viruses	Brachybacterium	Brachyspira	Bradyrhizobium	Brevibacillus	Brevibacterium	Brevundimonas	Brucella	Brugia	Buchnera	Bulleidia	Burkholderia	Butyrivibrio	Caenorhabditis	Caldanaerobacter	Caldicellulosiruptor	Calditerrivibrio	Caldivirga	Caminibacter	Campylobacter	Candida	Candidatus Accumulibacter	Candidatus Amoebophilus	Candidatus Azobacteroides	Candidatus Blochmannia	Candidatus Cloacamonas	Candidatus Desulforudis	Candidatus Hamiltonella	Candidatus Korarchaeum	Candidatus Koribacter	Candidatus Liberibacter	Candidatus Pelagibacter	Candidatus Phytoplasma	Candidatus Protochlamydia	Candidatus Puniceispirillum	Candidatus Regiella	Candidatus Riesia	Candidatus Solibacter	Candidatus Sulcia	Capnocytophaga	Carboxydothermus	Cardiobacterium	Carnobacterium	Catenibacterium	Catenulispora	Catonella	Caulobacter	Cellulomonas	Cellulosilyticum	Cellvibrio	Cenarchaeum	Chaetomium	Chelativorans	Chitinophaga	Chlamydia	Chlamydomonas	Chlamydophila	Chlorella	Chlorobaculum	Chlorobium	Chloroflexus	Chloroherpeton	Chlorovirus	Chromobacterium	Chromohalobacter	Chryseobacterium	Chthoniobacter	Citreicella	Citrobacter	Citromicrobium	Clavibacter	Clavispora	Clostridium	Coccidioides	Collinsella	Colwellia	Comamonas	Conexibacter	Congregibacter	Coprinopsis	Coprobacillus	Coprococcus	Coprothermobacter	Coraliomargarita	Corynebacterium	Coxiella	Croceibacter	Crocosphaera	Cronobacter	Cryptobacterium	Cryptosporidium	Cupriavidus	Cyanidioschyzon	Cyanidium	Cyanobium	Cyanophora	Cyanothece	Cylindrospermopsis	Cytophaga	Debaryomyces	Dechloromonas	Deferribacter	Dehalococcoides	Dehalogenimonas	Deinococcus	Delftia	Denitrovibrio	Dermacoccus	Desulfarculus	Desulfatibacillum	Desulfitobacterium	Desulfobacterium	Desulfococcus	Desulfohalobium	Desulfomicrobium	Desulfonatronospira	Desulfotalea	Desulfotomaculum	Desulfovibrio	Desulfurispirillum	Desulfurivibrio	Desulfurococcus	Desulfuromonas	Dethiobacter	Dethiosulfovibrio	Dialister	Dichelobacter	Dickeya	Dictyoglomus	Dictyostelium	Dinoroseobacter	Dokdonia	Dorea	Dyadobacter	Edwardsiella	Eggerthella	Ehrlichia	Eikenella	Elusimicrobium	Emericella	Emiliania	Encephalitozoon	Endoriftia	Enhydrobacter	Entamoeba	Enterobacter	Enterococcus	Enterocytozoon	Epsilon15-like viruses	Epulopiscium	Eremococcus	Eremothecium	Erwinia	Erysipelothrix	Erythrobacter	Escherichia	Ethanoligenens	Eubacterium	Exiguobacterium	Faecalibacterium	Ferrimonas	Ferroglobus	Ferroplasma	Fervidobacterium	Fibrobacter	Filifactor	Filobasidiella	Finegoldia	Flavobacterium	Francisella	Frankia	Fulvimarina	Fusobacterium	Gallionella	Gammaretrovirus	Gardnerella	Gemella	Gemmata	Gemmatimonas	Geobacillus	Geobacter	Geodermatophilus	Giardia	Gibberella	Gloeobacter	Gluconacetobacter	Gluconobacter	Gordonia	Gracilaria	Gramella	Granulibacter	Granulicatella	Guillardia	Haemophilus	Hahella	Halalkalicoccus	Halanaerobium	Haliangium	Haloarcula	Halobacterium	Haloferax	Halogeometricum	Halomicrobium	Halomonas	Haloquadratum	Halorhabdus	Halorhodospira	Halorubrum	Haloterrigena	Halothermothrix	Halothiobacillus	Helicobacter	Heliobacterium	Herbaspirillum	Herminiimonas	Herpetosiphon	Hirschia	Histophilus	Hoeflea	Holdemania	Hydrogenivirga	Hydrogenobacter	Hydrogenobaculum	Hyperthermus	Hyphomicrobium	Hyphomonas	Idiomarina	Ignicoccus	Ignisphaera	Ilyobacter	Intrasporangium	Janibacter	Jannaschia	Janthinobacterium	Jonesia	Jonquetella	Kangiella	Ketogulonicigenium	Kineococcus	Kingella	Klebsiella	Kluyveromyces	Kocuria	Kordia	Kosmotoga	Kribbella	Ktedonobacter	Kytococcus	L5-like viruses	Labrenzia	Laccaria	Lachancea	Lactobacillus	Lactococcus	Lambda-like viruses	Laribacter	Lawsonia	Leadbetterella	Leeuwenhoekiella	Legionella	Leifsonia	Leishmania	Lentisphaera	Leptosira	Leptospira	Leptospirillum	Leptothrix	Leptotrichia	Leuconostoc	Limnobacter	Listeria	Loa	Lodderomyces	Loktanella	Lutiella	Lyngbya	Lysinibacillus	Macrococcus	Magnaporthe	Magnetococcus	Magnetospirillum	Malassezia	Mannheimia	Maribacter	Maricaulis	Marinobacter	Marinomonas	Mariprofundus	Maritimibacter	Marivirga	Megasphaera	Meiothermus	Mesoplasma	Mesorhizobium	Metallosphaera	Methanobrevibacter	Methanocaldococcus	Methanocella	Methanococcoides	Methanococcus	Methanocorpusculum	Methanoculleus	Methanohalobium	Methanohalophilus	Methanoplanus	Methanopyrus	Methanoregula	Methanosaeta	Methanosarcina	Methanosphaera	Methanosphaerula	Methanospirillum	Methanothermobacter	Methanothermococcus	Methanothermus	Methylacidiphilum	Methylibium	Methylobacillus	Methylobacter	Methylobacterium	Methylocella	Methylococcus	Methylophaga	Methylosinus	Methylotenera	Methylovorus	Meyerozyma	Micrococcus	Microcoleus	Microcystis	Micromonas	Micromonospora	Microscilla	Mitsuokella	Mobiluncus	Moniliophthora	Monosiga	Moorella	Moraxella	Moritella	Mucilaginibacter	Mycobacterium	Mycoplasma	Myxococcus	N4-like viruses	Naegleria	Nakamurella	Nakaseomyces	Nanoarchaeum	Natranaerobius	Natrialba	Natronomonas	Nautilia	Nectria	Neisseria	Neorickettsia	Neosartorya	Neptuniibacter	Neurospora	Nitratiruptor	Nitrobacter	Nitrococcus	Nitrosococcus	Nitrosomonas	Nitrosopumilus	Nitrosospira	Nitrospira	Nocardia	Nocardioides	Nocardiopsis	Nodularia	Nostoc	Novosphingobium	Oceanibulbus	Oceanicaulis	Oceanicola	Oceanithermus	Oceanobacillus	Ochrobactrum	Octadecabacter	Odontella	Oenococcus	Oligotropha	Olsenella	Opitutus	Oribacterium	Orientia	Oscillatoria	Oscillochloris	Ostreococcus	Oxalobacter	P1-like viruses	P2-like viruses	P22-like viruses	Paenibacillus	Paludibacter	Pantoea	Parabacteroides	Parachlamydia	Paracoccidioides	Paracoccus	Paramecium	Parascardovia	Parvibaculum	Parvularcula	Pasteurella	Paulinella	Pectobacterium	Pediococcus	Pedobacter	Pelagibaca	Pelobacter	Pelodictyon	Pelotomaculum	Penicillium	Peptoniphilus	Peptostreptococcus	Perkinsus	Persephonella	Petrotoga	Phaeobacter	Phaeodactylum	Phaeosphaeria	Phenylobacterium	Phi29-like viruses	Photobacterium	Photorhabdus	Phytophthora	Pichia	Picrophilus	Pirellula	Planctomyces	Plasmodium	Plesiocystis	Podospora	Polaribacter	Polaromonas	Polynucleobacter	Porphyra	Porphyromonas	Postia	Prevotella	Prochlorococcus	Propionibacterium	Prosthecochloris	Proteus	Providencia	Pseudoalteromonas	Pseudomonas	Pseudoramibacter	Pseudovibrio	Psychrobacter	Psychroflexus	Psychromonas	Pyramidobacter	Pyrenophora	Pyrobaculum	Pyrococcus	Ralstonia	Raphidiopsis	Reclinomonas	Reinekea	Renibacterium	Rhizobium	Rhodobacter	Rhodococcus	Rhodomicrobium	Rhodomonas	Rhodopirellula	Rhodopseudomonas	Rhodospirillum	Rhodothermus	Rickettsia	Rickettsiella	Riemerella	Robiginitalea	Roseburia	Roseibium	Roseiflexus	Roseobacter	Roseomonas	Roseovarius	Rothia	Rubrobacter	Ruegeria	Ruminococcus	SP6-like viruses	SPO1-like viruses	SPbeta-like viruses	Saccharomonospora	Saccharomyces	Saccharophagus	Saccharopolyspora	Saccoglossus	Sagittula	Salinibacter	Salinispora	Salmonella	Sanguibacter	Scardovia	Scheffersomyces	Schizophyllum	Schizosaccharomyces	Sclerotinia	Sebaldella	Segniliparus	Selenomonas	Serratia	Shewanella	Shigella	Shuttleworthia	Sideroxydans	Simonsiella	Sinorhizobium	Slackia	Sodalis	Sorangium	Sphaerobacter	Sphingobacterium	Sphingobium	Sphingomonas	Sphingopyxis	Spirochaeta	Spirosoma	Stackebrandtia	Staphylococcus	Staphylothermus	Starkeya	Stenotrophomonas	Stigmatella	Streptobacillus	Streptococcus	Streptomyces	Streptosporangium	Subdoligranulum	Sulfitobacter	Sulfolobus	Sulfuricurvum	Sulfurihydrogenibium	Sulfurimonas	Sulfurospirillum	Sulfurovum	Symbiobacterium	Synechococcus	Synechocystis	Syntrophobacter	Syntrophomonas	Syntrophothermus	Syntrophus	T4-like viruses	T7-like viruses	Talaromyces	Teredinibacter	Terriglobus	Tetragenococcus	Tetrahymena	Thalassiosira	Thalassobium	Thauera	Theileria	Thermaerobacter	Thermanaerovibrio	Thermincola	Thermoanaerobacter	Thermoanaerobacterium	Thermobaculum	Thermobifida	Thermobispora	Thermococcus	Thermocrinis	Thermodesulfovibrio	Thermofilum	Thermomicrobium	Thermomonospora	Thermoplasma	Thermoproteus	Thermosediminibacter	Thermosinus	Thermosipho	Thermosphaera	Thermosynechococcus	Thermotoga	Thermus	Thioalkalivibrio	Thiobacillus	Thiomicrospira	Thiomonas	Tolumonas	Toxoplasma	Treponema	Trichodesmium	Trichomonas	Trichoplax	Tropheryma	Truepera	Trypanosoma	Tsukamurella	Tuber	Turicibacter	Uncinocarpus	Ureaplasma	Ustilago	Vanderwaltozyma	Variovorax	Veillonella	Verminephrobacter	Verrucomicrobium	Verticillium	Vibrio	Victivallis	Volvox	Vulcanisaeta	Waddlia	Weissella	Wigglesworthia	Wolbachia	Wolinella	Xanthobacter	Xanthomonas	Xenorhabdus	Xylanimonas	Xylella	Yarrowia	Yersinia	Zunongwangia	Zygosaccharomyces	Zymomonas	phiKZ-like viruses	unclassified (derived from Actinobacteria (class))	unclassified (derived from Alicyclobacillaceae)	unclassified (derived from Alphaproteobacteria)	unclassified (derived from Alteromonadales)	unclassified (derived from Bacteria)	unclassified (derived from Bacteroidetes)	unclassified (derived from Betaproteobacteria)	unclassified (derived from Burkholderiales)	unclassified (derived from Campylobacterales)	unclassified (derived from Candidatus Poribacteria)	unclassified (derived from Caudovirales)	unclassified (derived from Chroococcales)	unclassified (derived from Clostridiales Family XI. Incertae Sedis)	unclassified (derived from Clostridiales)	unclassified (derived from Deltaproteobacteria)	unclassified (derived from Elusimicrobia)	unclassified (derived from Erysipelotrichaceae)	unclassified (derived from Euryarchaeota)	unclassified (derived from Flavobacteria)	unclassified (derived from Flavobacteriaceae)	unclassified (derived from Flavobacteriales)	unclassified (derived from Gammaproteobacteria)	unclassified (derived from Lachnospiraceae)	unclassified (derived from Methylophilales)	unclassified (derived from Myoviridae)	unclassified (derived from Opitutaceae)	unclassified (derived from Podoviridae)	unclassified (derived from Rhodobacteraceae)	unclassified (derived from Rhodobacterales)	unclassified (derived from Rickettsiales)	unclassified (derived from Ruminococcaceae)	unclassified (derived from Siphoviridae)	unclassified (derived from Thermotogales)	unclassified (derived from Verrucomicrobia subdivision 3)	unclassified (derived from Verrucomicrobiales)	unclassified (derived from Vibrionaceae)	unclassified (derived from Vibrionales)	unclassified (derived from Viruses)	unclassified (derived from other sequences)
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	0.0013057	6.98e-05	0.0005123	1.70e-05	0.0002670	0.0002007	0.0000495	0.0129311	2.6e-06	1.52e-05	6.29e-05	6.31e-05	0.0000941	0.0001002	0.0002036	0.0000559	0.0002046	0.0006754	0.0002587	4.97e-05	0.0000879	6.40e-06	0.0002193	8.0e-06	5.90e-06	0.0000948	0.0001332	4.10e-06	2.8e-06	0.0003798	0.0000881	0.0000657	0.0000441	2.09e-05	0.0003706	0.0001340	0.0005110	0.0001064	0.0033871	5.10e-05	2.10e-06	0.0000381	0.0001312	2.60e-05	0.0002358	4.15e-05	0.0001219	3.35e-05	0.0011854	0.0003992	0.0003378	0.0011052	0.0017118	5.70e-06	0.0003584	0.0001428	0.0001368	0.0001188	0.0001322	0.0001062	0.0002451	3.6e-06	2.37e-05	4.30e-05	7.40e-05	0.0091799	2.89e-05	0.0000737	4.90e-05	4.25e-05	4.95e-05	2.1e-06	0.0070241	0.0821852	5.72e-05	0.0001917	0.0000861	2.09e-05	2.47e-05	7.00e-06	0.0001046	0.0105635	0.0000760	1.34e-05	0.0045629	0.0002440	0.0000750	4.90e-06	1.19e-05	0.0000776	0.0013351	0.0001835	0.0004561	0.0000719	3.25e-05	0.0000711	9.00e-06	2.22e-05	0.0005981	0.0006347	0.0103885	3.48e-05	0.0017562	0.0022862	0.0000760	7.20e-06	8.50e-06	0.0011851	1.08e-05	0.0000523	5.05e-05	0.0001809	1.31e-05	1.62e-05	0.0003760	1.24e-05	1.93e-05	0.0001729	5.9e-06	1.57e-05	4.35e-05	4.56e-05	1.19e-05	4.60e-06	1.8e-06	0.0004154	3.60e-06	0.0005886	0.0011439	1.86e-05	5.98e-05	0.0012611	6.44e-05	4.56e-05	0.0002100	0.0000750	0.0006844	0.0001497	3.90e-06	7.7e-06	0.0000706	0.0005677	3.53e-05	3.27e-05	3.48e-05	0.0e+00	0.0001706	0.0006079	0.0002337	0.0001206	1.0e-06	0.0001036	0.0000902	0.0000845	2.19e-05	3.30e-06	0.0001175	4.10e-06	0.0001051	4.40e-06	0.0767302	3.10e-06	0.0049257	0.0000662	0.0000307	8.35e-05	3.68e-05	6.70e-06	0.0003621	0.0063405	0.0000866	0.0000523	0.0017554	5.00e-05	0.0001224	6.31e-05	0.0000956	0.0010161	6.70e-06	0.0002183	2.1e-06	6.4e-06	0.0000031	1.3e-06	0.0004015	1.42e-05	0.0004213	8.2e-06	0.0000923	0.0001430	0.0009210	0.0000850	0.0002654	0.0000569	0.0001660	8.00e-06	0.0000796	0.0001523	0.0037569	0.0001355	0.0001580	0.0000680	0.0001482	1.80e-05	0.0001946	0.0029103	0.0012480	8.53e-05	5.82e-05	4.40e-06	0.0001660	0.0001098	0.0002556	0.0062201	8.50e-05	0.0001167	0.0003136	4.51e-05	3.50e-05	0.0000943	0.0052538	0.0004803	0.0000763	0.0027436	1.65e-05	7.20e-06	0.0000995	1.39e-05	0.0e+00	5.0e-07	3.30e-06	8.20e-06	0.0000255	0.0001585	0.0027601	1.21e-05	1.0e-06	0.0001392	2.99e-05	9.30e-06	0.0000727	0.0000740	0.0000858	0.0005095	0.0045129	0.0713607	0.0005494	0.0345703	0.0000340	3.25e-05	2.45e-05	0.0002023	0.0012287	0.0001531	1.88e-05	0.0007628	0.0012769	0.0001080	0.0002448	1.03e-05	0.0018682	3.38e-05	3.0e-07	0.0006563	4.02e-05	2.37e-05	0.0000544	0.0018128	0.0014026	5.33e-05	6.20e-06	2.47e-05	0.0001211	2.89e-05	6.36e-05	3.22e-05	5.0e-07	0.0003646	4.17e-05	0.0001232	7.2e-06	0.0003079	0.0001116	1.11e-05	0.0002358	0.0000729	2.24e-05	1.65e-05	1.34e-05	1.57e-05	1.00e-05	3.12e-05	1.52e-05	1.49e-05	5.82e-05	1.06e-05	1.96e-05	0.0005736	2.53e-05	0.0005641	0.0013552	3.79e-05	0.0000572	0.0001337	3.14e-05	0.0001221	8.00e-06	0.0034288	1.78e-05	3.66e-05	6.78e-05	1.11e-05	2.01e-05	4.30e-05	0.0000742	1.11e-05	6.7e-06	0.0006200	3.84e-05	2.86e-05	3.30e-05	0.0000580	0.0000861	0.0000698	2.81e-05	1.75e-05	0.0000925	8.80e-06	0.0002023	9.80e-06	0.0000673	2.96e-05	0.0000956	5.57e-05	3.61e-05	4.17e-05	3.0e-07	1.57e-05	3.3e-06	7.5e-06	0.0460339	0.0006994	0.0000167	3.74e-05	0.0000863	0.0003785	0.0003412	0.0001433	0.0000557	2.19e-05	2.63e-05	5e-07	0.0001330	3.0e-07	0.0000559	0.0006277	0.0004314	8.00e-06	0.0011256	2.10e-06	3.3e-06	1.08e-05	2.09e-05	1.86e-05	0.0002981	0.0001296	1.49e-05	0.0001492	0.0001649	6.70e-06	0.0000982	0.0002020	3.53e-05	0.0001242	0.0001456	1.21e-05	2.27e-05	0.0001381	0.0101970	0.0001415	0.0000500	0.0000794	1.11e-05	0.0004927	0.0001075	4.61e-05	0.0001031	0.0003092	0.0003340	0.0000987	3.30e-05	3.50e-05	0.0000711	2.71e-05	7.32e-05	0.0000693	0.0004033	0.0001286	5.49e-05	0.0001090	0.0000902	2.60e-06	1.16e-05	2.63e-05	0.0000466	5.26e-05	1.03e-05	0.0001515	5.10e-05	0.0000964	3.30e-06	6.20e-06	3.63e-05	4.46e-05	6.2e-06	5.67e-05	1.88e-05	8.86e-05	1.24e-05	5.64e-05	0.0000796	0.0274527	0.0015438	3.9e-06	1.47e-05	0.0011094	2.37e-05	0.0000129	0.0001693	0.0004409	0.0003685	0.0001484	5.0e-07	8.20e-06	5.67e-05	1.83e-05	1.0e-06	0.0004064	8.20e-06	1.60e-05	4.07e-05	6.2e-06	0.0002317	1.42e-05	3.99e-05	9.80e-06	2.47e-05	0.0001008	0.0000835	3.12e-05	0.0001214	0.0001090	1.06e-05	6.60e-05	4.92e-05	0.0000941	0.0001098	4.59e-05	1.78e-05	0.0001775	0.0000698	5.4e-06	2.53e-05	2.94e-05	4.07e-05	0.0004453	0.0000706	1.37e-05	2.1e-06	0.0002000	3.71e-05	0.0066190	0.0002461	0.0050074	1.16e-05	1.08e-05	2.04e-05	2.37e-05	0.0001325	0.0000000	3.0e-07	0.0e+00	0.0020159	0.0012415	0.0001355	0.0063554	8.20e-06	1.0e-06	8.68e-05	1.08e-05	0.0002180	6.62e-05	2.19e-05	0.0001057	1.3e-06	0.0001788	0.0004473	0.0008558	6.40e-06	0.0006842	0.0003234	0.0011539	1.29e-05	0.0004218	0.0003533	8.20e-06	5.72e-05	0.0001745	5.20e-06	9.30e-06	1.00e-05	4.84e-05	0.0e+00	0.0002054	0.0000863	1.62e-05	2.1e-06	1.47e-05	4.07e-05	0.0000966	0.0000356	3.04e-05	4.4e-06	0.0002322	0.0001095	0.0000495	5.4e-06	0.0022865	1.3e-06	0.2653352	0.0001701	0.0002247	3.74e-05	0.0000853	0.0000304	0.0001982	0.0007669	0.0014498	1.29e-05	0.0001301	4.90e-05	0.0001294	0.0004409	4.60e-06	2.50e-05	0.0001142	0.0001497	6.4e-06	1.5e-06	5.23e-05	3.63e-05	0.0002098	0.0001556	0.0001904	2.14e-05	1.0e-06	0.0001554	0.0002415	0.0001373	0.0001670	5.54e-05	2.30e-06	0.0001265	0.0001886	0.0240077	4.40e-06	0.0003873	0.0000938	9.80e-06	3.50e-05	0.0000856	0.0002105	0.0000768	0.0373864	1.3e-06	9.00e-06	2.8e-06	3.43e-05	1.39e-05	0.0001763	0.0001085	6.7e-06	7.70e-06	0.0001175	0.0001028	0.0001987	0.0001417	0.0001647	1.26e-05	8.8e-06	1.47e-05	3.6e-06	0.0006641	8.80e-06	0.0093160	0.0001337	0.0007009	0.0000611	0.0013000	5.44e-05	1.29e-05	0.0001417	0.0034399	2.60e-05	0.0001118	0.0001392	0.0001142	2.11e-05	7.47e-05	6.78e-05	0.0006087	0.0006646	4.59e-05	0.0011864	7.70e-06	2.53e-05	0.0000636	3.30e-05	0.0001755	0.0284657	0.0004780	0.0000657	0.0212210	1.24e-05	4.07e-05	3.61e-05	0.0001567	0.0000925	7.06e-05	5.98e-05	0.0009068	0.0005893	0.0001281	0.0002147	0.0009277	0.0003136	0.0002525	5.70e-06	0.0e+00	1.19e-05	4.43e-05	5.82e-05	2.8e-06	8.80e-06	1.57e-05	5.4e-06	4.25e-05	4.1e-06	0.0002438	0.0001904	0.0007352	0.0016657	0.0009148	0.0001428	0.0001144	5.75e-05	0.0001263	2.45e-05	0.0000925	2.19e-05	6.44e-05	5.10e-05	4.69e-05	1.5e-06	0.0004958	0.0006368	0.0002332	3.6e-06	0.0001193	0.0005569	0.0001469	0.0000801	0.0000760	0.0000613	2.55e-05	0.0000902	1.11e-05	0.0008166	0.0001051	0.0001180	1.75e-05	1.47e-05	4.48e-05	1.19e-05	3.61e-05	2.1e-06	0.0001995	2.3e-06	4.95e-05	1.24e-05	2.80e-06	0.0000472	0.0061611	0.0000701	1.67e-05	1.8e-06	0.0005584	0.0001708	2.53e-05	4.4e-06	1.83e-05	4.48e-05	2.60e-06	2.94e-05	0.0001108	6.11e-05	0.0001958	0.0000356	0.0001737	0.0000636	1.34e-05	0.0003090	0.0003956	2.3e-06	5.13e-05	1.0e-06	2.65e-05	0.0003023	1.44e-05	8.20e-06	0.0001495	0.0002438	2.8e-06	0.0000941	1.24e-05	6.70e-06	1.65e-05	8.50e-06	0.0001085	0.0032562	4.79e-05	6.34e-05	0.0045420	0.0000876	0.0001551	0.0001721	0.0000673	0.0001054	0.0042367	2.80e-06	0.0000531	0.0000222	1.80e-06	1.00e-05	1.00e-05	4.1e-06	0.0041192	0.0001979	2.96e-05	0.0000309	3.07e-05	1.44e-05	8.50e-06	6.49e-05	1.24e-05
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	0.0013478	9.90e-05	0.0007097	2.05e-05	0.0003269	0.0003021	0.0000709	0.0095018	4.8e-06	3.00e-05	8.19e-05	8.93e-05	0.0001283	0.0001319	0.0003576	0.0001186	0.0002488	0.0005859	0.0003412	6.69e-05	0.0000843	1.62e-05	0.0002676	7.9e-06	5.00e-06	0.0000990	0.0001931	1.07e-05	2.9e-06	0.0005195	0.0001507	0.0000793	0.0001486	3.55e-05	0.0004369	0.0001676	0.0014026	0.0000802	0.0046520	5.24e-05	4.30e-06	0.0000683	0.0002467	4.43e-05	0.0002855	5.26e-05	0.0001764	5.93e-05	0.0014702	0.0005607	0.0004626	0.0013019	0.0031158	1.14e-05	0.0004486	0.0001605	0.0002319	0.0001286	0.0001686	0.0001250	0.0003574	4.3e-06	3.00e-05	3.45e-05	7.79e-05	0.0101694	3.55e-05	0.0001119	5.95e-05	7.02e-05	7.14e-05	3.3e-06	0.0086887	0.1052813	8.05e-05	0.0002052	0.0001057	2.71e-05	3.90e-05	1.38e-05	0.0001431	0.0228505	0.0001224	3.95e-05	0.0056276	0.0002545	0.0001212	9.50e-06	3.30e-06	0.0001017	0.0015971	0.0002467	0.0005343	0.0000993	4.07e-05	0.0000938	1.12e-05	2.29e-05	0.0005540	0.0008445	0.0095013	4.86e-05	0.0021813	0.0028261	0.0001071	1.26e-05	1.57e-05	0.0020271	1.76e-05	0.0000609	8.98e-05	0.0004119	1.64e-05	3.81e-05	0.0004647	1.36e-05	2.31e-05	0.0002140	4.3e-06	1.88e-05	6.05e-05	6.76e-05	1.67e-05	5.70e-06	2.0e-07	0.0004440	1.17e-05	0.0009138	0.0012814	2.12e-05	8.05e-05	0.0018825	8.79e-05	6.48e-05	0.0002269	0.0000983	0.0009695	0.0001395	6.20e-06	4.0e-06	0.0001088	0.0008223	3.05e-05	3.57e-05	5.19e-05	1.4e-06	0.0002633	0.0006652	0.0002636	0.0001545	3.3e-06	0.0001426	0.0001190	0.0001619	8.93e-05	1.12e-05	0.0001638	7.60e-06	0.0001467	6.00e-06	0.1009617	1.31e-05	0.0048772	0.0000931	0.0000595	9.81e-05	4.02e-05	1.02e-05	0.0005309	0.0063733	0.0001293	0.0001126	0.0009138	5.43e-05	0.0002088	5.71e-05	0.0001119	0.0013080	1.76e-05	0.0002993	2.1e-06	4.3e-06	0.0000912	3.3e-06	0.0004788	1.33e-05	0.0006181	8.3e-06	0.0002207	0.0001400	0.0008804	0.0000902	0.0003302	0.0000831	0.0002276	1.33e-05	0.0001038	0.0002336	0.0048170	0.0001771	0.0002169	0.0000943	0.0001814	2.95e-05	0.0002493	0.0030973	0.0022225	9.90e-05	9.40e-05	8.30e-06	0.0001840	0.0001664	0.0003886	0.0024751	7.62e-05	0.0001355	0.0003700	8.93e-05	4.60e-05	0.0001848	0.0149870	0.0006012	0.0000940	0.0030911	2.45e-05	1.33e-05	0.0001614	1.48e-05	1.0e-06	2.6e-06	1.00e-05	1.60e-05	0.0003017	0.0001817	0.0035749	1.81e-05	2.1e-06	0.0002136	5.48e-05	1.00e-05	0.0000893	0.0001038	0.0001074	0.0010485	0.0059181	0.0601988	0.0006819	0.0296095	0.0000488	2.57e-05	2.71e-05	0.0002305	0.0016578	0.0002571	2.79e-05	0.0007926	0.0017283	0.0001786	0.0003407	9.30e-06	0.0025525	4.67e-05	2.0e-07	0.0012178	5.79e-05	4.26e-05	0.0000809	0.0021025	0.0016695	6.55e-05	1.40e-05	4.14e-05	0.0001609	5.26e-05	6.52e-05	5.88e-05	7.0e-07	0.0005845	5.36e-05	0.0001469	7.4e-06	0.0003728	0.0001350	1.45e-05	0.0002857	0.0000921	2.71e-05	2.14e-05	1.43e-05	2.29e-05	1.57e-05	4.10e-05	2.60e-05	2.14e-05	7.33e-05	1.19e-05	2.24e-05	0.0006481	3.71e-05	0.0008431	0.0014135	4.31e-05	0.0000831	0.0001567	2.79e-05	0.0001729	1.07e-05	0.0043720	2.29e-05	4.02e-05	7.74e-05	1.10e-05	3.17e-05	6.07e-05	0.0001157	1.43e-05	5.7e-06	0.0007131	3.55e-05	5.31e-05	4.81e-05	0.0000883	0.0001131	0.0000890	3.86e-05	2.88e-05	0.0001488	1.64e-05	0.0002228	1.71e-05	0.0000693	7.19e-05	0.0001533	8.24e-05	5.76e-05	5.71e-05	0.0e+00	3.02e-05	1.0e-05	7.9e-06	0.0365643	0.0006574	0.0000248	4.60e-05	0.0001417	0.0004093	0.0004226	0.0001619	0.0000962	3.05e-05	6.48e-05	2e-07	0.0001926	2.0e-07	0.0000995	0.0007257	0.0004550	1.76e-05	0.0013728	3.30e-06	6.0e-06	1.90e-05	2.57e-05	2.69e-05	0.0003719	0.0001431	2.17e-05	0.0001840	0.0001967	8.30e-06	0.0000921	0.0003433	6.14e-05	0.0001674	0.0001752	2.02e-05	3.14e-05	0.0002436	0.0059802	0.0002057	0.0000633	0.0001245	1.36e-05	0.0006671	0.0001657	5.26e-05	0.0001343	0.0004228	0.0004162	0.0001257	3.69e-05	5.52e-05	0.0001081	4.19e-05	8.33e-05	0.0000955	0.0005721	0.0001462	5.29e-05	0.0001212	0.0001283	6.40e-06	2.33e-05	4.93e-05	0.0001126	8.43e-05	2.00e-05	0.0002076	4.10e-05	0.0001271	1.17e-05	2.24e-05	5.50e-05	3.81e-05	7.6e-06	6.79e-05	3.38e-05	9.76e-05	1.76e-05	7.88e-05	0.0001602	0.0218968	0.0024935	5.5e-06	2.10e-05	0.0011928	2.64e-05	0.0000190	0.0002362	0.0005878	0.0004047	0.0002167	3.6e-06	2.67e-05	7.76e-05	1.93e-05	1.2e-06	0.0005021	1.74e-05	2.36e-05	5.26e-05	8.3e-06	0.0003097	1.50e-05	4.31e-05	1.79e-05	2.31e-05	0.0001198	0.0001093	4.24e-05	0.0001821	0.0001531	1.29e-05	8.31e-05	7.31e-05	0.0001100	0.0002026	6.57e-05	1.98e-05	0.0002267	0.0000931	7.1e-06	3.21e-05	3.79e-05	5.79e-05	0.0005259	0.0001162	1.64e-05	7.0e-07	0.0002638	2.43e-05	0.0072738	0.0003702	0.0017714	1.93e-05	2.26e-05	3.40e-05	3.83e-05	0.0001764	0.0000005	3.8e-06	4.5e-06	0.0027108	0.0021187	0.0001431	0.0093044	1.12e-05	1.7e-06	8.05e-05	1.69e-05	0.0002426	8.71e-05	3.36e-05	0.0001257	3.6e-06	0.0002074	0.0005376	0.0013526	8.30e-06	0.0008312	0.0003002	0.0013007	1.48e-05	0.0006295	0.0004781	1.02e-05	6.57e-05	0.0002369	6.20e-06	1.98e-05	1.29e-05	4.12e-05	1.9e-06	0.0002690	0.0001145	3.71e-05	5.5e-06	2.60e-05	6.90e-05	0.0001586	0.0000562	3.86e-05	9.5e-06	0.0003917	0.0002409	0.0000940	5.5e-06	0.0039265	1.7e-06	0.1953868	0.0002638	0.0002686	6.26e-05	0.0001017	0.0000571	0.0002417	0.0009345	0.0005771	1.76e-05	0.0001795	7.81e-05	0.0001679	0.0007288	6.20e-06	3.19e-05	0.0001490	0.0001812	5.7e-06	5.0e-07	8.31e-05	4.86e-05	0.0002498	0.0001893	0.0002359	3.60e-05	7.0e-07	0.0002340	0.0003250	0.0001838	0.0002314	7.29e-05	2.40e-06	0.0002000	0.0002702	0.0171608	9.30e-06	0.0005097	0.0001350	2.52e-05	5.79e-05	0.0000945	0.0002457	0.0001267	0.0555783	0.0e+00	1.00e-05	2.6e-06	4.86e-05	2.57e-05	0.0002005	0.0001519	4.3e-06	1.26e-05	0.0001588	0.0001309	0.0002948	0.0001664	0.0002036	2.07e-05	9.5e-06	3.90e-05	2.9e-06	0.0008007	1.62e-05	0.0080618	0.0001619	0.0009002	0.0001214	0.0010104	4.19e-05	2.19e-05	0.0002159	0.0041267	4.00e-05	0.0001348	0.0002038	0.0002421	4.48e-05	8.76e-05	7.76e-05	0.0009776	0.0008926	6.52e-05	0.0013411	1.38e-05	4.24e-05	0.0001002	4.90e-05	0.0002655	0.0077406	0.0007007	0.0001212	0.0155818	2.43e-05	5.05e-05	4.90e-05	0.0001390	0.0001029	8.17e-05	9.93e-05	0.0010376	0.0010085	0.0001517	0.0002898	0.0011535	0.0003964	0.0003407	2.02e-05	8.3e-06	6.40e-06	5.93e-05	9.86e-05	4.5e-06	1.33e-05	2.33e-05	6.9e-06	5.79e-05	4.5e-06	0.0003150	0.0002264	0.0008414	0.0019737	0.0009819	0.0001759	0.0001381	8.05e-05	0.0001938	3.10e-05	0.0001164	2.88e-05	9.24e-05	7.69e-05	7.14e-05	1.7e-06	0.0006600	0.0011904	0.0002948	2.9e-06	0.0001426	0.0007307	0.0001933	0.0001059	0.0001067	0.0000852	3.17e-05	0.0001143	9.80e-06	0.0012692	0.0001229	0.0003755	1.62e-05	2.14e-05	6.67e-05	2.88e-05	3.50e-05	6.4e-06	0.0002702	5.5e-06	5.74e-05	2.33e-05	5.50e-06	0.0000769	0.0047134	0.0001209	7.21e-05	5.7e-06	0.0007326	0.0003717	3.95e-05	7.1e-06	3.62e-05	5.05e-05	3.60e-06	4.76e-05	0.0001538	7.59e-05	0.0002807	0.0000402	0.0001895	0.0000779	2.60e-05	0.0003219	0.0004664	6.4e-06	6.40e-05	1.4e-06	4.98e-05	0.0003367	3.36e-05	1.05e-05	0.0001955	0.0005247	4.0e-06	0.0001940	1.71e-05	1.17e-05	4.07e-05	1.12e-05	0.0001238	0.0040044	8.45e-05	7.83e-05	0.0067576	0.0001309	0.0002533	0.0002098	0.0001726	0.0001759	0.0050081	3.80e-06	0.0001398	0.0001421	7.90e-06	2.02e-05	2.26e-05	2.9e-06	0.0122500	0.0002545	4.55e-05	0.0000800	6.05e-05	1.90e-05	7.90e-06	7.40e-05	7.90e-06
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14	0.0010663	6.91e-05	0.0005364	1.55e-05	0.0002629	0.0002116	0.0000510	0.0082861	6.5e-06	2.10e-05	6.29e-05	5.75e-05	0.0000800	0.0000780	0.0002916	0.0000635	0.0001691	0.0004657	0.0001808	4.69e-05	0.0001909	4.70e-06	0.0001973	6.7e-06	3.10e-06	0.0000748	0.0001147	3.40e-06	2.8e-06	0.0003675	0.0001202	0.0000782	0.0000704	3.65e-05	0.0003546	0.0001259	0.0007555	0.0000842	0.0034668	4.48e-05	1.30e-06	0.0000505	0.0001572	2.90e-05	0.0002165	5.05e-05	0.0001256	3.81e-05	0.0010849	0.0003916	0.0003149	0.0010981	0.0022428	1.14e-05	0.0003667	0.0001215	0.0001254	0.0001054	0.0001474	0.0000995	0.0002015	3.6e-06	2.30e-05	2.87e-05	5.52e-05	0.0051601	2.18e-05	0.0000875	4.77e-05	6.16e-05	3.96e-05	1.6e-06	0.0067811	0.0763643	4.40e-05	0.0001453	0.0000816	1.29e-05	2.49e-05	9.80e-06	0.0000575	0.0065677	0.0000738	2.33e-05	0.0048146	0.0002178	0.0000637	5.40e-06	3.10e-06	0.0000578	0.0012535	0.0001707	0.0004266	0.0000531	2.98e-05	0.0000583	7.80e-06	1.97e-05	0.0003965	0.0006247	0.0089022	2.69e-05	0.0017150	0.0024133	0.0000720	5.40e-06	9.60e-06	0.0012351	1.42e-05	0.0000544	5.59e-05	0.0002046	8.00e-06	2.25e-05	0.0003934	1.01e-05	1.35e-05	0.0001797	2.6e-06	2.07e-05	3.88e-05	4.74e-05	1.68e-05	2.80e-06	5.0e-07	0.0003660	8.00e-06	0.0006288	0.0010722	1.79e-05	6.99e-05	0.0009637	5.39e-05	6.37e-05	0.0002082	0.0000526	0.0007143	0.0001274	5.40e-06	8.0e-06	0.0000800	0.0005915	1.94e-05	1.89e-05	4.22e-05	8.0e-07	0.0001784	0.0005144	0.0002326	0.0001580	1.8e-06	0.0000969	0.0000870	0.0000873	4.35e-05	8.00e-06	0.0001217	7.00e-06	0.0000868	1.01e-05	0.0774401	4.70e-06	0.0053002	0.0000837	0.0000438	7.59e-05	3.03e-05	3.60e-06	0.0003274	0.0064006	0.0000922	0.0000640	0.0004708	3.76e-05	0.0001127	4.87e-05	0.0000834	0.0008477	7.50e-06	0.0002261	1.6e-06	4.1e-06	0.0001197	2.6e-06	0.0004048	1.22e-05	0.0004131	3.9e-06	0.0000956	0.0000987	0.0008091	0.0000811	0.0002502	0.0000637	0.0001968	7.30e-06	0.0000774	0.0001564	0.0038092	0.0001290	0.0001515	0.0000875	0.0001367	1.84e-05	0.0002129	0.0026608	0.0014037	7.02e-05	6.14e-05	5.70e-06	0.0001525	0.0001274	0.0002318	0.0019038	6.32e-05	0.0001140	0.0002872	3.96e-05	3.06e-05	0.0001072	0.0083552	0.0004289	0.0000616	0.0024832	1.53e-05	9.30e-06	0.0001171	1.01e-05	1.0e-06	5.0e-07	3.60e-06	1.37e-05	0.0000293	0.0001238	0.0034243	1.40e-05	1.0e-06	0.0001512	2.67e-05	8.50e-06	0.0000790	0.0000541	0.0000800	0.0007941	0.0051174	0.0696863	0.0005392	0.0454851	0.0000368	2.07e-05	1.53e-05	0.0002056	0.0012911	0.0001608	1.92e-05	0.0006146	0.0012753	0.0001318	0.0002471	6.70e-06	0.0017461	4.17e-05	5.0e-07	0.0004649	4.45e-05	2.07e-05	0.0000624	0.0016751	0.0013395	4.58e-05	6.20e-06	2.69e-05	0.0001388	3.55e-05	4.40e-05	2.67e-05	1.3e-06	0.0003551	4.56e-05	0.0001699	6.5e-06	0.0002611	0.0000956	1.14e-05	0.0002357	0.0000710	2.23e-05	1.19e-05	9.60e-06	1.48e-05	9.10e-06	2.67e-05	2.02e-05	1.06e-05	5.23e-05	1.27e-05	1.94e-05	0.0005309	2.77e-05	0.0005602	0.0013338	5.34e-05	0.0000699	0.0001352	2.54e-05	0.0001479	7.00e-06	0.0032586	2.28e-05	2.56e-05	4.71e-05	9.10e-06	1.97e-05	5.13e-05	0.0000844	9.10e-06	4.1e-06	0.0005457	2.49e-05	3.03e-05	3.70e-05	0.0000767	0.0000629	0.0000546	2.72e-05	2.15e-05	0.0000699	1.14e-05	0.0001554	9.10e-06	0.0000401	4.33e-05	0.0000963	4.79e-05	3.24e-05	3.16e-05	3.0e-07	1.58e-05	3.6e-06	4.1e-06	0.0424407	0.0011250	0.0000127	4.14e-05	0.0000963	0.0003903	0.0002950	0.0001254	0.0000484	1.58e-05	3.99e-05	3e-07	0.0001375	8.0e-07	0.0000697	0.0006397	0.0005550	1.58e-05	0.0011362	3.10e-06	4.4e-06	1.61e-05	1.63e-05	2.43e-05	0.0003113	0.0001347	1.68e-05	0.0001375	0.0001279	5.20e-06	0.0000699	0.0001976	3.91e-05	0.0001217	0.0001199	1.55e-05	2.90e-05	0.0001404	0.0070940	0.0001349	0.0000502	0.0000805	8.80e-06	0.0005001	0.0001083	3.78e-05	0.0001152	0.0003175	0.0002989	0.0001114	2.67e-05	3.03e-05	0.0000831	3.13e-05	6.40e-05	0.0000694	0.0003955	0.0000984	4.43e-05	0.0000966	0.0000888	9.80e-06	1.40e-05	3.57e-05	0.0000736	5.90e-05	1.58e-05	0.0001634	4.20e-05	0.0000974	8.30e-06	1.99e-05	3.52e-05	2.75e-05	6.0e-06	4.58e-05	1.89e-05	9.74e-05	1.45e-05	5.75e-05	0.0000901	0.0148157	0.0003079	3.1e-06	1.42e-05	0.0010292	2.05e-05	0.0000104	0.0001142	0.0003781	0.0003056	0.0001492	5.0e-07	9.60e-06	4.97e-05	1.50e-05	1.0e-06	0.0003986	1.32e-05	1.76e-05	4.12e-05	5.4e-06	0.0002108	8.30e-06	2.93e-05	9.30e-06	1.24e-05	0.0000769	0.0000919	3.26e-05	0.0001248	0.0001051	9.80e-06	6.24e-05	5.49e-05	0.0000704	0.0001028	4.09e-05	1.63e-05	0.0001816	0.0000948	5.2e-06	1.76e-05	2.95e-05	3.96e-05	0.0004079	0.0000570	1.35e-05	1.8e-06	0.0002269	1.92e-05	0.0035111	0.0003020	0.0020639	2.02e-05	1.86e-05	2.49e-05	2.90e-05	0.0001507	0.0000018	8.0e-07	3.0e-07	0.0020722	0.0013423	0.0001046	0.0061947	5.70e-06	8.0e-07	5.26e-05	1.04e-05	0.0000956	6.68e-05	2.95e-05	0.0000710	7.8e-06	0.0001577	0.0004610	0.0008676	5.40e-06	0.0007184	0.0002629	0.0010857	8.50e-06	0.0003854	0.0003142	4.90e-06	5.67e-05	0.0001709	4.70e-06	1.27e-05	5.70e-06	4.69e-05	3.0e-07	0.0002201	0.0000746	1.42e-05	3.4e-06	2.15e-05	5.93e-05	0.0001028	0.0000287	2.54e-05	5.2e-06	0.0002346	0.0002020	0.0000702	8.3e-06	0.0020786	1.3e-06	0.2305057	0.0002497	0.0001601	4.38e-05	0.0000710	0.0000381	0.0001836	0.0006775	0.0005781	1.04e-05	0.0001204	4.38e-05	0.0001347	0.0003996	2.30e-06	3.08e-05	0.0001083	0.0001492	5.2e-06	5.0e-07	5.44e-05	2.46e-05	0.0001771	0.0001494	0.0001683	2.28e-05	1.8e-06	0.0001500	0.0002357	0.0001533	0.0001634	5.78e-05	1.80e-06	0.0001173	0.0002080	0.0215569	6.20e-06	0.0004234	0.0000901	1.61e-05	3.26e-05	0.0000883	0.0001805	0.0000976	0.0341373	1.3e-06	8.50e-06	3.4e-06	2.90e-05	1.86e-05	0.0002256	0.0000862	4.1e-06	7.30e-06	0.0001202	0.0000982	0.0001968	0.0001013	0.0000974	8.50e-06	8.0e-06	1.61e-05	3.4e-06	0.0005734	1.09e-05	0.0096929	0.0001329	0.0006918	0.0001075	0.0009518	3.60e-05	2.93e-05	0.0001518	0.0028976	3.16e-05	0.0001096	0.0001500	0.0001334	2.80e-05	8.21e-05	6.45e-05	0.0005936	0.0006374	3.65e-05	0.0011680	1.32e-05	2.56e-05	0.0000557	3.52e-05	0.0002131	0.1117937	0.0004628	0.0000761	0.0196324	1.97e-05	2.87e-05	3.65e-05	0.0001295	0.0000914	6.68e-05	6.89e-05	0.0008067	0.0010129	0.0001285	0.0001942	0.0009562	0.0003017	0.0002357	9.30e-06	5.2e-06	2.30e-06	3.96e-05	6.40e-05	6.2e-06	8.30e-06	1.89e-05	3.4e-06	4.20e-05	7.8e-06	0.0002497	0.0001639	0.0006892	0.0015045	0.0007671	0.0001254	0.0000919	4.58e-05	0.0001378	2.20e-05	0.0000901	2.41e-05	7.87e-05	5.65e-05	4.53e-05	2.1e-06	0.0005076	0.0009085	0.0002375	6.5e-06	0.0001228	0.0005633	0.0001331	0.0000697	0.0000839	0.0000647	2.95e-05	0.0000738	3.60e-06	0.0007803	0.0000927	0.0001207	8.00e-06	1.04e-05	4.33e-05	1.68e-05	2.41e-05	4.9e-06	0.0001660	2.6e-06	3.94e-05	7.30e-06	2.30e-06	0.0000756	0.0044696	0.0000826	3.63e-05	3.4e-06	0.0005467	0.0001606	2.82e-05	4.4e-06	2.87e-05	5.67e-05	3.90e-06	3.34e-05	0.0000953	6.24e-05	0.0001994	0.0000334	0.0001158	0.0000552	1.09e-05	0.0002763	0.0003709	3.1e-06	6.37e-05	1.8e-06	2.69e-05	0.0002730	2.10e-05	7.50e-06	0.0001494	0.0002119	1.3e-06	0.0001740	1.06e-05	5.40e-06	1.63e-05	9.60e-06	0.0000826	0.0029442	5.36e-05	6.40e-05	0.0049519	0.0000805	0.0001564	0.0001533	0.0000894	0.0001339	0.0043629	4.10e-06	0.0000554	0.0000422	4.90e-06	1.63e-05	1.24e-05	2.3e-06	0.0055364	0.0002965	3.47e-05	0.0000412	3.57e-05	8.80e-06	4.10e-06	4.71e-05	1.61e-05
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	14	Tomato	Day 7	Tomato Day 7	0.0013116	1.09e-04	0.0008422	3.41e-05	0.0003321	0.0003254	0.0000749	0.0094690	5.8e-06	2.25e-05	8.32e-05	8.49e-05	0.0001123	0.0000940	0.0002788	0.0001623	0.0003678	0.0008322	0.0003370	7.57e-05	0.0001123	6.70e-06	0.0010511	8.3e-06	1.25e-05	0.0002189	0.0001914	1.08e-05	5.0e-06	0.0004810	0.0001298	0.0001523	0.0000866	2.91e-05	0.0004619	0.0003254	0.0011734	0.0001099	0.0044083	9.15e-05	2.00e-05	0.0001639	0.0002014	4.58e-05	0.0002314	4.33e-05	0.0001872	7.32e-05	0.0013116	0.0006342	0.0003803	0.0016178	0.0029386	1.50e-05	0.0004427	0.0001415	0.0003687	0.0001806	0.0002022	0.0001290	0.0003379	3.3e-06	3.66e-05	3.58e-05	5.16e-05	0.0029136	3.16e-05	0.0001323	5.58e-05	6.57e-05	8.99e-05	2.5e-06	0.0083622	0.0892998	6.57e-05	0.0002755	0.0001232	4.16e-05	3.08e-05	5.08e-05	0.0001689	0.0046122	0.0000874	3.33e-05	0.0059104	0.0002480	0.0001773	2.41e-05	3.30e-06	0.0001423	0.0015346	0.0001997	0.0004985	0.0001007	3.83e-05	0.0001332	4.49e-05	9.90e-05	0.0005776	0.0008663	0.0090321	5.49e-05	0.0021047	0.0029727	0.0001015	5.80e-06	1.75e-05	0.0017227	1.75e-05	0.0000699	6.99e-05	0.0002929	2.66e-05	3.00e-05	0.0004402	3.58e-05	3.00e-05	0.0001997	7.5e-06	2.66e-05	8.07e-05	4.91e-05	1.75e-05	2.83e-05	5.0e-06	0.0003537	9.20e-06	0.0007041	0.0012392	3.41e-05	8.41e-05	0.0011451	8.66e-05	6.99e-05	0.0001872	0.0001074	0.0009454	0.0001598	1.25e-05	5.0e-06	0.0000741	0.0006317	2.33e-05	3.00e-05	4.16e-05	8.0e-07	0.0002122	0.0006133	0.0002563	0.0001798	3.3e-06	0.0001481	0.0001440	0.0001140	2.75e-05	1.00e-05	0.0003412	4.20e-06	0.0001706	5.80e-06	0.0910882	7.50e-06	0.0026098	0.0001781	0.0000516	9.40e-05	4.58e-05	1.17e-05	0.0003920	0.0047045	0.0001182	0.0000682	0.0026390	8.41e-05	0.0001531	7.99e-05	0.0002230	0.0007665	1.58e-05	0.0002663	2.5e-06	4.2e-06	0.0000108	2.5e-06	0.0004827	1.91e-05	0.0004752	5.0e-06	0.0001606	0.0001357	0.0009454	0.0001298	0.0003088	0.0000674	0.0002147	2.00e-05	0.0001123	0.0002380	0.0042968	0.0001664	0.0002114	0.0001148	0.0001723	3.25e-05	0.0002530	0.0028811	0.0032182	9.49e-05	9.32e-05	1.08e-05	0.0001997	0.0001823	0.0003262	0.0011618	9.15e-05	0.0002405	0.0003529	7.82e-05	4.91e-05	0.0001806	0.0068667	0.0004660	0.0002139	0.0023435	3.16e-05	1.91e-05	0.0002056	2.33e-05	4.2e-06	5.0e-06	1.08e-05	3.50e-05	0.0000399	0.0003728	0.0036326	1.41e-05	2.5e-06	0.0002413	5.91e-05	1.41e-05	0.0001748	0.0001015	0.0000924	0.0100665	0.0060960	0.0458013	0.0005767	0.0398517	0.0001631	2.91e-05	3.66e-05	0.0002122	0.0014564	0.0002538	2.83e-05	0.0011035	0.0014522	0.0002372	0.0002746	9.20e-06	0.0024900	5.41e-05	0.0e+00	0.0004253	9.82e-05	2.58e-05	0.0000508	0.0020597	0.0016245	7.91e-05	5.80e-06	3.41e-05	0.0001465	5.49e-05	4.83e-05	5.74e-05	1.7e-06	0.0005176	6.66e-05	0.0001581	7.5e-06	0.0007066	0.0001806	2.00e-05	0.0002530	0.0001115	3.50e-05	2.16e-05	1.91e-05	2.33e-05	1.17e-05	5.66e-05	2.41e-05	2.66e-05	9.15e-05	1.83e-05	2.91e-05	0.0006084	6.49e-05	0.0029286	0.0014098	7.74e-05	0.0001015	0.0001664	2.75e-05	0.0002971	1.83e-05	0.0036768	3.74e-05	4.74e-05	6.24e-05	1.50e-05	2.08e-05	5.66e-05	0.0002505	2.41e-05	1.0e-05	0.0005934	4.33e-05	6.32e-05	4.91e-05	0.0001148	0.0001132	0.0001315	6.66e-05	2.41e-05	0.0001881	2.16e-05	0.0005077	1.58e-05	0.0001165	8.57e-05	0.0001523	8.82e-05	6.91e-05	7.49e-05	0.0e+00	4.99e-05	8.3e-06	6.7e-06	0.0870628	0.0006733	0.0001173	7.49e-05	0.0001997	0.0003620	0.0004244	0.0002155	0.0000866	3.08e-05	7.07e-05	8e-07	0.0001972	0.0e+00	0.0000882	0.0006200	0.0004536	3.41e-05	0.0013740	1.50e-05	5.0e-06	2.08e-05	3.00e-05	2.00e-05	0.0003878	0.0001714	2.41e-05	0.0001756	0.0001872	1.33e-05	0.0001556	0.0003370	5.58e-05	0.0002480	0.0002314	3.00e-05	3.00e-05	0.0001956	0.0051190	0.0001906	0.0001074	0.0000915	1.91e-05	0.0022853	0.0002272	7.24e-05	0.0001490	0.0004469	0.0004003	0.0001273	4.49e-05	6.74e-05	0.0001015	6.57e-05	9.99e-05	0.0001032	0.0006366	0.0001631	7.49e-05	0.0001381	0.0003004	1.91e-05	4.83e-05	5.33e-05	0.0000816	8.90e-05	3.66e-05	0.0001947	3.99e-05	0.0001515	2.33e-05	1.83e-05	6.91e-05	5.66e-05	8.3e-06	5.83e-05	2.25e-05	9.65e-05	2.33e-05	6.99e-05	0.0001273	0.0064530	0.0042194	4.2e-06	3.83e-05	0.0011343	4.83e-05	0.0001132	0.0001848	0.0005975	0.0004952	0.0001947	1.7e-06	2.25e-05	8.07e-05	3.25e-05	5.0e-06	0.0004985	1.08e-05	1.75e-05	5.91e-05	1.0e-05	0.0004253	1.58e-05	4.58e-05	3.74e-05	2.50e-05	0.0001090	0.0001165	4.08e-05	0.0002130	0.0001989	1.83e-05	9.65e-05	7.66e-05	0.0001173	0.0001823	5.58e-05	2.00e-05	0.0002139	0.0000666	1.0e-05	2.58e-05	3.16e-05	5.99e-05	0.0004977	0.0000782	2.16e-05	3.3e-06	0.0002380	3.16e-05	0.0019391	0.0002455	0.0028795	1.83e-05	3.66e-05	2.41e-05	4.49e-05	0.0003146	0.0001049	5.0e-06	4.2e-06	0.0024667	0.0015330	0.0002413	0.0087625	1.58e-05	8.0e-07	6.49e-05	7.50e-06	0.0000641	8.99e-05	4.24e-05	0.0002538	5.8e-06	0.0003071	0.0005118	0.0010345	8.30e-06	0.0007898	0.0002522	0.0010827	1.00e-05	0.0007490	0.0005110	1.41e-05	5.66e-05	0.0002538	1.08e-05	2.66e-05	1.33e-05	4.41e-05	8.0e-07	0.0005709	0.0002413	3.16e-05	6.7e-06	4.16e-05	4.49e-05	0.0001257	0.0001057	4.74e-05	5.0e-06	0.0002630	0.0001748	0.0000832	7.5e-06	0.0028529	8.0e-07	0.2312992	0.0002297	0.0004169	4.49e-05	0.0002430	0.0001323	0.0005426	0.0012242	0.0005243	1.58e-05	0.0002189	6.57e-05	0.0003961	0.0005368	1.08e-05	4.41e-05	0.0001806	0.0001997	5.8e-06	3.3e-06	9.40e-05	5.74e-05	0.0002355	0.0001573	0.0002480	3.74e-05	8.0e-07	0.0001656	0.0003645	0.0002372	0.0002056	9.40e-05	1.66e-05	0.0001456	0.0002646	0.0101514	3.30e-06	0.0004635	0.0001573	1.83e-05	4.49e-05	0.0001207	0.0002155	0.0001223	0.0483745	8.0e-07	1.25e-05	2.5e-06	6.32e-05	3.41e-05	0.0002255	0.0001165	4.2e-06	9.20e-06	0.0001390	0.0001307	0.0008505	0.0001648	0.0000674	1.66e-05	9.2e-06	3.91e-05	5.0e-06	0.0006849	1.50e-05	0.0092501	0.0002788	0.0015912	0.0012483	0.0009063	5.24e-05	4.49e-05	0.0001856	0.0028062	8.82e-05	0.0001015	0.0002239	0.0001748	5.16e-05	9.99e-05	6.32e-05	0.0009812	0.0006733	6.08e-05	0.0026731	1.83e-05	4.74e-05	0.0001257	3.83e-05	0.0002264	0.0075358	0.0006533	0.0001157	0.0210211	2.25e-05	6.99e-05	5.08e-05	0.0001581	0.0000999	9.32e-05	9.82e-05	0.0009737	0.0007082	0.0001365	0.0002788	0.0011318	0.0003412	0.0002929	1.50e-05	4.2e-06	7.50e-06	8.66e-05	6.66e-05	7.5e-06	1.25e-05	2.41e-05	5.0e-06	5.49e-05	9.2e-06	0.0002971	0.0002413	0.0008139	0.0019957	0.0008655	0.0001631	0.0001615	7.24e-05	0.0002014	3.50e-05	0.0001240	2.75e-05	9.24e-05	9.15e-05	8.41e-05	2.5e-06	0.0006816	0.0012350	0.0002971	4.2e-06	0.0001315	0.0006699	0.0001956	0.0001357	0.0000965	0.0001215	5.33e-05	0.0005576	6.70e-06	0.0017984	0.0001440	0.0002089	1.58e-05	2.16e-05	4.99e-05	1.58e-05	3.99e-05	5.0e-06	0.0003379	6.7e-06	6.66e-05	1.91e-05	9.20e-06	0.0000558	0.0039406	0.0000699	3.74e-05	5.8e-06	0.0014372	0.0003387	4.91e-05	7.5e-06	5.58e-05	6.08e-05	1.41e-05	6.16e-05	0.0002164	7.16e-05	0.0003021	0.0001049	0.0001723	0.0001007	2.50e-05	0.0006425	0.0004169	3.3e-06	5.24e-05	5.0e-06	5.33e-05	0.0003137	2.66e-05	6.41e-05	0.0007731	0.0003437	8.3e-06	0.0003146	1.58e-05	1.41e-05	4.91e-05	1.41e-05	0.0001731	0.0036817	7.74e-05	9.32e-05	0.0045048	0.0001615	0.0002364	0.0001689	0.0001939	0.0002971	0.0046013	1.08e-05	0.0000691	0.0000499	2.50e-06	3.41e-05	6.66e-05	4.2e-06	0.0092876	0.0003287	7.74e-05	0.0000458	3.91e-05	8.82e-05	2.58e-05	7.32e-05	9.57e-05
ShotgunWGS-ControlPig5GutMicrobiome-Day7	5	Control	Day 7	Control Day 7	0.0012072	7.49e-05	0.0006482	2.08e-05	0.0002852	0.0002562	0.0000902	0.0096632	5.2e-06	2.93e-05	6.67e-05	7.91e-05	0.0001110	0.0000990	0.0003871	0.0001449	0.0002917	0.0005789	0.0002028	5.83e-05	0.0000729	1.53e-05	0.0005483	8.8e-06	1.01e-05	0.0001247	0.0001608	9.80e-06	6.2e-06	0.0003953	0.0001673	0.0001110	0.0000983	4.95e-05	0.0003809	0.0002106	0.0011405	0.0000879	0.0039948	6.67e-05	2.30e-06	0.0001087	0.0002058	3.42e-05	0.0002653	5.47e-05	0.0001459	4.53e-05	0.0010314	0.0004802	0.0003692	0.0012463	0.0028801	1.40e-05	0.0003718	0.0001426	0.0001703	0.0001426	0.0001498	0.0001354	0.0002393	1.6e-06	2.87e-05	2.83e-05	4.07e-05	0.0041645	3.22e-05	0.0001322	5.99e-05	7.16e-05	7.33e-05	2.0e-06	0.0072689	0.0733720	6.71e-05	0.0002191	0.0000957	2.18e-05	3.29e-05	2.08e-05	0.0000710	0.0215087	0.0000977	3.13e-05	0.0054932	0.0002702	0.0000853	7.80e-06	2.00e-06	0.0000661	0.0013678	0.0002178	0.0004516	0.0000697	3.42e-05	0.0000840	1.17e-05	6.58e-05	0.0003572	0.0008638	0.0094401	3.65e-05	0.0018766	0.0026456	0.0000794	9.80e-06	1.33e-05	0.0019229	1.17e-05	0.0001003	7.52e-05	0.0002631	1.86e-05	2.70e-05	0.0004040	1.86e-05	2.15e-05	0.0001853	8.5e-06	2.34e-05	5.70e-05	5.76e-05	1.73e-05	8.80e-06	1.6e-06	0.0003002	1.01e-05	0.0006124	0.0011304	2.51e-05	6.41e-05	0.0012369	6.28e-05	5.80e-05	0.0001778	0.0000622	0.0009132	0.0001084	7.80e-06	5.2e-06	0.0000794	0.0005675	2.80e-05	3.48e-05	3.68e-05	1.3e-06	0.0002136	0.0005825	0.0002299	0.0001426	2.9e-06	0.0001296	0.0001185	0.0000967	7.49e-05	1.01e-05	0.0001530	9.40e-06	0.0001009	2.60e-06	0.0886641	1.01e-05	0.0033316	0.0001162	0.0000632	9.67e-05	4.88e-05	6.20e-06	0.0003871	0.0061049	0.0000973	0.0000983	0.0005945	4.95e-05	0.0001410	4.88e-05	0.0001107	0.0008725	1.37e-05	0.0003116	2.0e-06	5.2e-06	0.0001953	1.3e-06	0.0004184	1.33e-05	0.0004356	4.6e-06	0.0002680	0.0001211	0.0008107	0.0000736	0.0002686	0.0001071	0.0001791	1.14e-05	0.0000908	0.0001980	0.0043077	0.0001374	0.0001680	0.0000850	0.0001449	1.82e-05	0.0002191	0.0027140	0.0024063	8.89e-05	6.77e-05	7.80e-06	0.0001556	0.0001696	0.0003060	0.0042192	7.94e-05	0.0001563	0.0002920	5.01e-05	3.97e-05	0.0001195	0.0107353	0.0004021	0.0001354	0.0025304	2.02e-05	1.56e-05	0.0001465	1.47e-05	1.0e-06	1.3e-06	1.20e-05	1.63e-05	0.0000716	0.0001644	0.0029729	2.47e-05	2.0e-06	0.0001755	4.23e-05	1.27e-05	0.0000967	0.0000726	0.0000964	0.0005408	0.0053714	0.0589401	0.0005489	0.0373568	0.0000879	2.54e-05	3.06e-05	0.0002038	0.0013153	0.0002188	1.60e-05	0.0007654	0.0010939	0.0001726	0.0002898	1.17e-05	0.0021713	5.93e-05	7.0e-07	0.0011454	5.63e-05	3.32e-05	0.0000687	0.0017737	0.0014238	5.34e-05	1.17e-05	2.47e-05	0.0001211	4.33e-05	4.98e-05	3.74e-05	0.0e+00	0.0004001	4.69e-05	0.0001400	4.9e-06	0.0004304	0.0001504	1.14e-05	0.0002432	0.0000902	2.80e-05	2.67e-05	1.37e-05	1.79e-05	1.27e-05	4.30e-05	1.66e-05	2.60e-05	6.87e-05	1.30e-05	2.25e-05	0.0005639	3.61e-05	0.0006163	0.0013590	7.81e-05	0.0000980	0.0001325	3.45e-05	0.0001957	1.14e-05	0.0035511	2.21e-05	3.26e-05	4.75e-05	1.33e-05	2.08e-05	6.12e-05	0.0001546	1.37e-05	7.8e-06	0.0006264	3.13e-05	4.40e-05	4.40e-05	0.0001130	0.0000733	0.0000791	6.22e-05	2.41e-05	0.0001022	1.99e-05	0.0002061	7.50e-06	0.0000423	5.01e-05	0.0001149	5.47e-05	4.62e-05	4.10e-05	7.0e-07	2.34e-05	6.2e-06	5.5e-06	0.1788730	0.0006108	0.0000264	6.12e-05	0.0001481	0.0003174	0.0003262	0.0001784	0.0000563	2.05e-05	5.67e-05	7e-07	0.0001579	0.0e+00	0.0000967	0.0006316	0.0003917	3.09e-05	0.0012903	4.20e-06	7.8e-06	1.66e-05	3.16e-05	2.18e-05	0.0003080	0.0001374	1.53e-05	0.0001374	0.0001944	5.90e-06	0.0000866	0.0002360	4.13e-05	0.0002015	0.0001843	2.44e-05	3.16e-05	0.0001589	0.0052210	0.0001566	0.0000560	0.0001074	1.63e-05	0.0006486	0.0001735	6.87e-05	0.0001107	0.0003865	0.0003588	0.0001175	3.09e-05	4.46e-05	0.0000970	3.84e-05	8.37e-05	0.0000957	0.0005125	0.0001104	4.66e-05	0.0001208	0.0001293	8.10e-06	2.64e-05	4.23e-05	0.0001205	9.83e-05	2.38e-05	0.0002002	4.59e-05	0.0001299	1.95e-05	3.42e-05	6.38e-05	4.79e-05	4.2e-06	3.91e-05	2.21e-05	8.33e-05	1.86e-05	7.03e-05	0.0001032	0.0165768	0.0003607	2.3e-06	1.82e-05	0.0011503	3.48e-05	0.0000544	0.0001628	0.0004845	0.0003305	0.0001605	2.0e-06	1.47e-05	5.18e-05	1.99e-05	1.3e-06	0.0004503	1.79e-05	2.44e-05	4.92e-05	5.9e-06	0.0002989	1.07e-05	3.09e-05	2.93e-05	1.60e-05	0.0000964	0.0001029	3.78e-05	0.0001638	0.0001514	1.01e-05	9.60e-05	6.28e-05	0.0000674	0.0001345	5.27e-05	2.02e-05	0.0001996	0.0000944	6.5e-06	2.47e-05	3.61e-05	4.66e-05	0.0004350	0.0000713	1.95e-05	1.3e-06	0.0002256	2.96e-05	0.0029943	0.0002793	0.0015901	1.11e-05	2.38e-05	2.87e-05	3.65e-05	0.0002859	0.0000007	1.0e-06	7.0e-07	0.0023002	0.0012714	0.0001608	0.0071679	9.80e-06	2.0e-06	7.36e-05	1.27e-05	0.0002260	7.42e-05	3.26e-05	0.0001774	9.1e-06	0.0002246	0.0003835	0.0008931	1.11e-05	0.0006974	0.0002618	0.0011070	9.10e-06	0.0004975	0.0004252	1.17e-05	5.60e-05	0.0001966	5.20e-06	1.53e-05	9.80e-06	3.35e-05	1.0e-06	0.0003484	0.0001599	2.60e-05	3.9e-06	3.32e-05	7.98e-05	0.0001416	0.0000283	3.09e-05	3.9e-06	0.0002618	0.0002728	0.0001182	6.2e-06	0.0025476	2.0e-06	0.1651427	0.0002930	0.0002146	4.82e-05	0.0001400	0.0000824	0.0003090	0.0009214	0.0005196	1.73e-05	0.0001494	4.92e-05	0.0002377	0.0005493	2.90e-06	3.22e-05	0.0001390	0.0002292	6.8e-06	7.0e-07	6.87e-05	3.71e-05	0.0002116	0.0002188	0.0002009	2.67e-05	1.0e-06	0.0001927	0.0003321	0.0001905	0.0001885	6.97e-05	9.40e-06	0.0001322	0.0002061	0.0153119	1.11e-05	0.0004421	0.0001289	2.83e-05	5.18e-05	0.0000697	0.0001914	0.0001286	0.0422350	1.3e-06	1.27e-05	7.0e-07	3.65e-05	2.31e-05	0.0001872	0.0001143	2.6e-06	1.11e-05	0.0001234	0.0001127	0.0002725	0.0001127	0.0001856	1.50e-05	8.5e-06	1.92e-05	4.9e-06	0.0006779	1.66e-05	0.0068267	0.0001872	0.0010484	0.0000680	0.0009028	5.80e-05	2.21e-05	0.0001973	0.0030057	5.67e-05	0.0001107	0.0001895	0.0001761	5.18e-05	9.90e-05	7.55e-05	0.0007739	0.0005623	5.31e-05	0.0013756	1.76e-05	3.58e-05	0.0000866	4.53e-05	0.0002100	0.0133708	0.0005942	0.0000876	0.0141685	1.86e-05	4.07e-05	3.81e-05	0.0001328	0.0000964	8.47e-05	9.31e-05	0.0009048	0.0013508	0.0001280	0.0002507	0.0009702	0.0003187	0.0002953	1.37e-05	2.3e-06	6.80e-06	5.57e-05	5.99e-05	2.0e-06	7.80e-06	1.92e-05	6.2e-06	8.30e-05	4.6e-06	0.0002735	0.0001993	0.0007003	0.0017239	0.0008599	0.0001302	0.0001061	6.80e-05	0.0001748	2.80e-05	0.0000944	2.67e-05	8.14e-05	6.02e-05	7.07e-05	3.6e-06	0.0005691	0.0008680	0.0002946	1.6e-06	0.0001087	0.0006219	0.0001566	0.0001133	0.0001055	0.0000912	3.61e-05	0.0003002	7.20e-06	0.0011174	0.0001091	0.0001520	8.80e-06	1.92e-05	5.37e-05	1.50e-05	3.39e-05	4.6e-06	0.0002653	4.2e-06	5.21e-05	1.20e-05	5.90e-06	0.0001048	0.0042954	0.0001094	5.93e-05	1.3e-06	0.0009344	0.0003793	3.22e-05	7.2e-06	3.22e-05	3.87e-05	7.50e-06	4.72e-05	0.0000964	8.24e-05	0.0002540	0.0000628	0.0001169	0.0000964	1.69e-05	0.0003506	0.0003139	5.5e-06	6.25e-05	3.9e-06	4.33e-05	0.0003220	4.04e-05	3.68e-05	0.0002735	0.0003340	9.8e-06	0.0001953	1.40e-05	7.20e-06	3.32e-05	8.50e-06	0.0001201	0.0034580	6.19e-05	7.00e-05	0.0043846	0.0001260	0.0001605	0.0001384	0.0001136	0.0001976	0.0048931	1.04e-05	0.0000563	0.0000619	2.00e-06	2.41e-05	3.09e-05	3.3e-06	0.0079575	0.0002338	4.20e-05	0.0000641	4.53e-05	3.97e-05	1.33e-05	6.51e-05	4.20e-06
ShotgunWGS-TomatoPig18GutMicrobiome-Day7	18	Tomato	Day 7	Tomato Day 7	0.0006501	8.19e-05	0.0003281	1.85e-05	0.0001486	0.0001896	0.0001094	0.0059482	3.4e-06	4.77e-05	7.80e-05	7.57e-05	0.0000959	0.0001464	0.0006512	0.0000813	0.0002064	0.0003556	0.0002956	9.65e-05	0.0000561	2.75e-05	0.0001997	6.7e-06	7.30e-06	0.0000645	0.0001498	9.50e-06	5.0e-06	0.0004022	0.0002872	0.0000583	0.0001049	7.74e-05	0.0002737	0.0001032	0.0009177	0.0000780	0.0024922	5.05e-05	5.16e-05	0.0000415	0.0001099	1.91e-05	0.0001262	8.30e-05	0.0001285	2.97e-05	0.0007864	0.0002524	0.0003326	0.0008061	0.0016144	1.51e-05	0.0002620	0.0001027	0.0001369	0.0000830	0.0001088	0.0001027	0.0003999	1.1e-06	2.08e-05	4.43e-05	5.83e-05	0.0085061	2.64e-05	0.0001105	6.17e-05	5.89e-05	4.71e-05	2.8e-06	0.0049581	0.0944738	3.76e-05	0.0001200	0.0000819	1.46e-05	4.09e-05	1.12e-05	0.0001139	0.0585603	0.0000987	2.52e-05	0.0040758	0.0002507	0.0000752	9.50e-06	7.30e-06	0.0000931	0.0011628	0.0002592	0.0002816	0.0001212	5.72e-05	0.0000752	2.75e-05	1.80e-05	0.0002120	0.0007567	0.0061681	3.25e-05	0.0012464	0.0018191	0.0000611	7.90e-06	6.20e-06	0.0006967	5.60e-06	0.0000724	4.88e-05	0.0002345	1.07e-05	1.40e-05	0.0002406	4.50e-06	1.40e-05	0.0001975	3.9e-06	2.41e-05	2.69e-05	3.87e-05	1.51e-05	2.20e-06	0.0e+00	0.0004336	5.60e-06	0.0006372	0.0006221	1.12e-05	3.20e-05	0.0006283	9.98e-05	3.25e-05	0.0002098	0.0001127	0.0004213	0.0001251	6.20e-06	2.2e-06	0.0000954	0.0006995	2.19e-05	3.59e-05	2.86e-05	0.0e+00	0.0001868	0.0005901	0.0002087	0.0001402	9.0e-06	0.0000970	0.0000740	0.0001139	9.42e-05	7.30e-06	0.0002575	1.07e-05	0.0002227	8.40e-06	0.0567714	6.70e-06	0.0087798	0.0000836	0.0001027	8.41e-05	3.76e-05	6.70e-06	0.0002182	0.0031340	0.0000567	0.0000965	0.0010394	4.09e-05	0.0001144	3.93e-05	0.0001060	0.0009912	5.60e-06	0.0003332	6.0e-07	3.4e-06	0.0003949	2.8e-06	0.0003444	1.12e-05	0.0004426	6.2e-06	0.0001515	0.0000583	0.0005839	0.0001290	0.0002485	0.0001391	0.0001301	2.08e-05	0.0000595	0.0001172	0.0027587	0.0001094	0.0001335	0.0000611	0.0000976	1.85e-05	0.0001677	0.0016553	0.0010131	5.10e-05	6.45e-05	7.90e-06	0.0001217	0.0000578	0.0001761	0.0018118	5.67e-05	0.0001111	0.0002373	3.48e-05	4.49e-05	0.0001234	0.0048387	0.0004465	0.0000724	0.0024844	1.68e-05	1.12e-05	0.0000869	1.57e-05	0.0e+00	6.0e-07	3.40e-06	7.90e-06	0.0000174	0.0001975	0.0023369	1.68e-05	0.0e+00	0.0000954	2.30e-05	1.57e-05	0.0000898	0.0000359	0.0001200	0.0090541	0.0048308	0.0372300	0.0003798	0.0766393	0.0000337	1.96e-05	1.74e-05	0.0001374	0.0011617	0.0000948	2.24e-05	0.0004942	0.0016200	0.0001251	0.0004297	1.01e-05	0.0013552	3.70e-05	2.8e-06	0.0027166	2.47e-05	5.10e-05	0.0001167	0.0011976	0.0011370	8.98e-05	2.20e-06	3.93e-05	0.0001296	4.09e-05	5.95e-05	5.16e-05	6.0e-07	0.0003803	7.52e-05	0.0000836	3.4e-06	0.0002418	0.0000948	7.90e-06	0.0001257	0.0000808	3.03e-05	1.12e-05	7.90e-06	1.12e-05	1.18e-05	3.37e-05	1.46e-05	1.23e-05	8.02e-05	7.90e-06	2.19e-05	0.0003607	3.81e-05	0.0005200	0.0007629	9.70e-05	0.0000797	0.0001369	2.24e-05	0.0001027	8.40e-06	0.0027587	1.07e-05	1.46e-05	3.98e-05	7.90e-06	4.71e-05	5.55e-05	0.0000684	5.00e-06	3.4e-06	0.0003579	7.29e-05	9.37e-05	4.94e-05	0.0001105	0.0001335	0.0000578	3.03e-05	2.47e-05	0.0001829	5.00e-06	0.0003046	1.29e-05	0.0000639	4.82e-05	0.0000797	9.82e-05	3.48e-05	6.00e-05	1.1e-06	2.52e-05	2.8e-06	9.0e-06	0.1042975	0.0005228	0.0001430	4.15e-05	0.0001015	0.0003433	0.0004684	0.0001329	0.0001071	1.74e-05	2.86e-05	0e+00	0.0001318	1.1e-06	0.0001879	0.0004465	0.0003758	4.04e-05	0.0009199	1.51e-05	6.2e-06	2.36e-05	1.68e-05	1.63e-05	0.0002059	0.0000735	7.30e-06	0.0001127	0.0001509	5.60e-06	0.0000684	0.0002485	4.60e-05	0.0001257	0.0001167	1.29e-05	2.58e-05	0.0001357	0.0064643	0.0001402	0.0000370	0.0001481	1.12e-05	0.0003876	0.0000987	3.93e-05	0.0000892	0.0002395	0.0002721	0.0000959	2.19e-05	2.08e-05	0.0000645	3.14e-05	6.84e-05	0.0000533	0.0002850	0.0000959	3.87e-05	0.0000791	0.0000825	1.07e-05	1.01e-05	4.04e-05	0.0002311	7.24e-05	2.80e-05	0.0002474	7.24e-05	0.0001245	8.40e-06	7.01e-05	4.04e-05	3.98e-05	5.6e-06	4.99e-05	2.30e-05	8.13e-05	1.46e-05	8.47e-05	0.0000864	0.0049536	0.0005694	3.4e-06	1.35e-05	0.0006754	1.63e-05	0.0000191	0.0001784	0.0007500	0.0002221	0.0001834	0.0e+00	1.57e-05	8.86e-05	2.13e-05	6.0e-07	0.0002850	1.07e-05	7.90e-06	2.36e-05	5.0e-06	0.0001778	7.90e-06	3.20e-05	1.07e-05	9.50e-06	0.0000628	0.0001172	3.31e-05	0.0001285	0.0001464	8.40e-06	6.28e-05	4.54e-05	0.0000993	0.0002754	6.84e-05	1.29e-05	0.0001946	0.0001335	7.3e-06	3.03e-05	4.66e-05	4.43e-05	0.0002765	0.0000774	1.23e-05	0.0e+00	0.0001930	3.48e-05	0.0061911	0.0004028	0.0009514	7.30e-06	2.19e-05	2.02e-05	2.92e-05	0.0000864	0.0000673	1.1e-06	8.4e-06	0.0015790	0.0017703	0.0000987	0.0087316	8.40e-06	6.0e-07	6.23e-05	1.29e-05	0.0004970	8.02e-05	3.03e-05	0.0000639	9.5e-06	0.0001380	0.0004347	0.0009979	1.68e-05	0.0005082	0.0002822	0.0006815	7.30e-06	0.0002535	0.0002137	1.40e-05	4.21e-05	0.0001397	6.70e-06	2.02e-05	9.50e-06	3.98e-05	1.1e-06	0.0001616	0.0000869	1.40e-05	3.9e-06	2.19e-05	9.42e-05	0.0001700	0.0000409	3.93e-05	6.2e-06	0.0002889	0.0005312	0.0001862	3.4e-06	0.0026594	2.2e-06	0.2682445	0.0004056	0.0003629	3.37e-05	0.0000746	0.0000337	0.0001874	0.0008173	0.0003281	8.40e-06	0.0000993	5.22e-05	0.0000993	0.0003136	3.90e-06	2.36e-05	0.0001004	0.0002132	6.2e-06	1.1e-06	4.09e-05	6.23e-05	0.0002042	0.0002563	0.0002760	3.20e-05	0.0e+00	0.0002008	0.0003012	0.0001526	0.0001767	7.18e-05	2.20e-06	0.0001027	0.0002047	0.0088533	9.50e-06	0.0004117	0.0001498	3.76e-05	5.16e-05	0.0000864	0.0002025	0.0001212	0.0264808	6.0e-07	5.00e-06	3.9e-06	4.60e-05	6.51e-05	0.0002350	0.0002031	1.1e-06	1.07e-05	0.0001105	0.0001851	0.0006002	0.0001700	0.0003214	1.35e-05	2.2e-06	2.24e-05	4.5e-06	0.0004824	1.29e-05	0.0013463	0.0001386	0.0006204	0.0011297	0.0005278	4.71e-05	3.59e-05	0.0001986	0.0028008	2.58e-05	0.0001077	0.0002216	0.0001481	7.12e-05	1.29e-04	9.48e-05	0.0004717	0.0008341	5.67e-05	0.0016879	1.29e-05	4.43e-05	0.0000813	6.34e-05	0.0002008	0.0057126	0.0008779	0.0001212	0.0215249	2.52e-05	2.97e-05	2.41e-05	0.0001419	0.0000561	3.93e-05	4.21e-05	0.0006226	0.0019566	0.0001156	0.0001705	0.0007130	0.0001666	0.0001784	2.08e-05	2.8e-06	2.20e-06	4.94e-05	4.54e-05	2.2e-06	5.60e-06	1.57e-05	3.4e-06	6.45e-05	1.7e-06	0.0001924	0.0001251	0.0003489	0.0011656	0.0005121	0.0001111	0.0001694	8.98e-05	0.0000959	1.74e-05	0.0000909	2.30e-05	7.91e-05	8.47e-05	4.94e-05	2.2e-06	0.0003237	0.0003781	0.0001750	5.6e-06	0.0001060	0.0004005	0.0001228	0.0000909	0.0000847	0.0000471	4.99e-05	0.0000729	5.60e-06	0.0008106	0.0000875	0.0001043	1.63e-05	2.13e-05	6.23e-05	1.35e-05	4.66e-05	5.6e-06	0.0001178	1.7e-06	3.76e-05	2.13e-05	1.46e-05	0.0001733	0.0028142	0.0001761	8.47e-05	1.7e-06	0.0005396	0.0001329	4.04e-05	3.4e-06	2.69e-05	3.81e-05	6.20e-06	3.59e-05	0.0000701	8.64e-05	0.0002721	0.0000376	0.0001840	0.0000595	1.01e-05	0.0003068	0.0004129	6.7e-06	6.06e-05	2.8e-06	7.80e-05	0.0001761	2.97e-05	5.60e-06	0.0006776	0.0002193	2.8e-06	0.0000668	8.40e-06	9.50e-06	2.30e-05	6.70e-06	0.0000707	0.0024249	4.71e-05	4.21e-05	0.0022275	0.0000791	0.0001526	0.0001638	0.0000920	0.0001531	0.0029915	3.40e-06	0.0000589	0.0000561	1.07e-05	1.80e-05	1.40e-05	4.5e-06	0.0069882	0.0002317	2.69e-05	0.0001021	4.54e-05	6.70e-06	5.00e-06	4.88e-05	3.31e-05

# move Sample_Name to rownames, remove metadata
RelAbund.Genus.Filt.zerofilt.alphadiv <- RelAbund.Genus.Filt.zerofilt

rownames(RelAbund.Genus.Filt.zerofilt.alphadiv) <- RelAbund.Genus.Filt.zerofilt.alphadiv$Sample_Name  

RelAbund.Genus.Filt.zerofilt.alphadiv[1:5,1:8]

	Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	Abiotrophia	Acaryochloris	Acetivibrio
ShotgunWGS-ControlPig6GutMicrobiome-Day14	ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	0.0013057	6.98e-05	0.0005123
ShotgunWGS-ControlPig8GutMicrobiome-Day0	ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	0.0013478	9.90e-05	0.0007097
ShotgunWGS-ControlPig3GutMicrobiome-Day14	ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14	0.0010663	6.91e-05	0.0005364
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	ShotgunWGS-TomatoPig14GutMicrobiome-Day7	14	Tomato	Day 7	Tomato Day 7	0.0013116	1.09e-04	0.0008422
ShotgunWGS-ControlPig5GutMicrobiome-Day7	ShotgunWGS-ControlPig5GutMicrobiome-Day7	5	Control	Day 7	Control Day 7	0.0012072	7.49e-05	0.0006482

# remove metadata
RelAbund.Genus.Filt.zerofilt.alphadiv <- RelAbund.Genus.Filt.zerofilt.alphadiv %>%
  select(Abiotrophia:ncol(.))

RelAbund.Genus.Filt.zerofilt.alphadiv[1:5,1:5]

	Abiotrophia	Acaryochloris	Acetivibrio	Acetobacter	Acetohalobium
ShotgunWGS-ControlPig6GutMicrobiome-Day14	0.0013057	6.98e-05	0.0005123	1.70e-05	0.0002670
ShotgunWGS-ControlPig8GutMicrobiome-Day0	0.0013478	9.90e-05	0.0007097	2.05e-05	0.0003269
ShotgunWGS-ControlPig3GutMicrobiome-Day14	0.0010663	6.91e-05	0.0005364	1.55e-05	0.0002629
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	0.0013116	1.09e-04	0.0008422	3.41e-05	0.0003321
ShotgunWGS-ControlPig5GutMicrobiome-Day7	0.0012072	7.49e-05	0.0006482	2.08e-05	0.0002852

rownames(RelAbund.Genus.Filt.zerofilt.alphadiv)

##  [1] "ShotgunWGS-ControlPig6GutMicrobiome-Day14" 
##  [2] "ShotgunWGS-ControlPig8GutMicrobiome-Day0"  
##  [3] "ShotgunWGS-ControlPig3GutMicrobiome-Day14" 
##  [4] "ShotgunWGS-TomatoPig14GutMicrobiome-Day7"  
##  [5] "ShotgunWGS-ControlPig5GutMicrobiome-Day7"  
##  [6] "ShotgunWGS-TomatoPig18GutMicrobiome-Day7"  
##  [7] "ShotgunWGS-TomatoPig16GutMicrobiome-Day7"  
##  [8] "ShotgunWGS-ControlPig10GutMicrobiome-Day7" 
##  [9] "ShotgunWGS-ControlPig2GutMicrobiome-Day0"  
## [10] "ShotgunWGS-TomatoPig18GutMicrobiome-Day0"  
## [11] "ShotgunWGS-ControlPig10GutMicrobiome-Day0" 
## [12] "ShotgunWGS-ControlPig7GutMicrobiome-Day0"  
## [13] "ShotgunWGS-ControlPig8GutMicrobiome-Day14" 
## [14] "ShotgunWGS-TomatoPig11GutMicrobiome-Day0"  
## [15] "ShotgunWGS-TomatoPig19GutMicrobiome-Day0"  
## [16] "ShotgunWGS-TomatoPig17GutMicrobiome-Day14" 
## [17] "ShotgunWGS-ControlPig9GutMicrobiome-Day14" 
## [18] "ShotgunWGS-ControlPig10GutMicrobiome-Day14"
## [19] "ShotgunWGS-TomatoPig19GutMicrobiome-Day7"  
## [20] "ShotgunWGS-ControlPig5GutMicrobiome-Day14" 
## [21] "ShotgunWGS-ControlPig2GutMicrobiome-Day7"  
## [22] "ShotgunWGS-ControlPig6GutMicrobiome-Day7"  
## [23] "ShotgunWGS-TomatoPig12GutMicrobiome-Day0"  
## [24] "ShotgunWGS-TomatoPig14GutMicrobiome-Day0"  
## [25] "ShotgunWGS-ControlPig7GutMicrobiome-Day14" 
## [26] "ShotgunWGS-TomatoPig11GutMicrobiome-Day14" 
## [27] "ShotgunWGS-TomatoPig20GutMicrobiome-Day0"  
## [28] "ShotgunWGS-ControlPig9GutMicrobiome-Day0"  
## [29] "ShotgunWGS-TomatoPig11GutMicrobiome-Day7"  
## [30] "ShotgunWGS-TomatoPig13GutMicrobiome-Day7"  
## [31] "ShotgunWGS-TomatoPig17GutMicrobiome-Day0"  
## [32] "ShotgunWGS-TomatoPig19GutMicrobiome-Day14" 
## [33] "ShotgunWGS-TomatoPig13GutMicrobiome-Day0"  
## [34] "ShotgunWGS-ControlPig2GutMicrobiome-Day14" 
## [35] "ShotgunWGS-ControlPig1GutMicrobiome-Day7"  
## [36] "ShotgunWGS-TomatoPig15GutMicrobiome-Day7"  
## [37] "ShotgunWGS-TomatoPig15GutMicrobiome-Day0"  
## [38] "ShotgunWGS-TomatoPig12GutMicrobiome-Day7"  
## [39] "ShotgunWGS-TomatoPig14GutMicrobiome-Day14" 
## [40] "ShotgunWGS-TomatoPig20GutMicrobiome-Day14" 
## [41] "ShotgunWGS-ControlPig1GutMicrobiome-Day0"  
## [42] "ShotgunWGS-ControlPig4GutMicrobiome-Day14" 
## [43] "ShotgunWGS-ControlPig6GutMicrobiome-Day0"  
## [44] "ShotgunWGS-TomatoPig16GutMicrobiome-Day0"  
## [45] "ShotgunWGS-TomatoPig16GutMicrobiome-Day14" 
## [46] "ShotgunWGS-TomatoPig18GutMicrobiome-Day14" 
## [47] "ShotgunWGS-ControlPig7GutMicrobiome-Day7"  
## [48] "ShotgunWGS-ControlPig4GutMicrobiome-Day7"  
## [49] "ShotgunWGS-TomatoPig13GutMicrobiome-Day14" 
## [50] "ShotgunWGS-ControlPig8GutMicrobiome-Day7"  
## [51] "ShotgunWGS-TomatoPig15GutMicrobiome-Day14" 
## [52] "ShotgunWGS-TomatoPig12GutMicrobiome-Day14" 
## [53] "ShotgunWGS-TomatoPig20GutMicrobiome-Day7"  
## [54] "ShotgunWGS-ControlPig1GutMicrobiome-Day14" 
## [55] "ShotgunWGS-ControlPig3GutMicrobiome-Day0"  
## [56] "ShotgunWGS-ControlPig5GutMicrobiome-Day0"  
## [57] "ShotgunWGS-ControlPig4GutMicrobiome-Day0"  
## [58] "ShotgunWGS-ControlPig9GutMicrobiome-Day7"  
## [59] "ShotgunWGS-ControlPig3GutMicrobiome-Day7"  
## [60] "ShotgunWGS-TomatoPig17GutMicrobime-Day7"

2.7.2 Calculate alpha diversity

# run alpha diversity on phyla
genera.filt.div <- diversity(RelAbund.Genus.Filt.zerofilt.alphadiv, index = "shannon")

# convert to df
genera.filt.div.df <- as.data.frame(genera.filt.div)

# make column name 'shannon.phyla.filt'
colnames(genera.filt.div.df) <- "shannon.genera.filt"

head(genera.filt.div.df)

	shannon.genera.filt
ShotgunWGS-ControlPig6GutMicrobiome-Day14	3.454094
ShotgunWGS-ControlPig8GutMicrobiome-Day0	3.724200
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3.405044
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	3.639925
ShotgunWGS-ControlPig5GutMicrobiome-Day7	3.526991
ShotgunWGS-TomatoPig18GutMicrobiome-Day7	3.281356

Combine shannon alpha diversity results with metadata

# compile genera metadata
genera.metadata <- RelAbund.Genus.Filt.zerofilt[,1:5]

# combine with metadata
genera.filt.div.df.meta <- cbind(genera.metadata, genera.filt.div.df)

head(genera.filt.div.df.meta)

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	shannon.genera.filt
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	3.454094
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	3.724200
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14	3.405044
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	14	Tomato	Day 7	Tomato Day 7	3.639925
ShotgunWGS-ControlPig5GutMicrobiome-Day7	5	Control	Day 7	Control Day 7	3.526991
ShotgunWGS-TomatoPig18GutMicrobiome-Day7	18	Tomato	Day 7	Tomato Day 7	3.281356

2.7.3 Plotting

X axis by diet

alpha.diversity.genera.bydiet <- genera.filt.div.df.meta %>%
  ggplot(aes(x = Diet, y = shannon.genera.filt, fill = Diet_By_Time_Point)) +
  geom_boxplot(outlier.shape = NA) +
  geom_point(aes(fill = Diet_By_Time_Point), color = "black", alpha = 0.7, position=position_jitterdodge()) +
  scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  scale_color_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, color = "black")) +
  labs(x=NULL, 
       y="Shannon diversity index", 
       title = "Alpha Diversity",
       subtitle = "Shannon Index, Genera Level", 
       fill="Diet & Time Point")

alpha.diversity.genera.bydiet

ggsave("Figures/AlphaDiversityGenera_ByDiet_Boxplot.png", 
       plot = alpha.diversity.genera.bydiet, 
       dpi = 800, 
       width = 10, 
       height = 6)

X-axis by Day

genera.filt.div.df.meta <- genera.filt.div.df.meta %>%
  mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))

alpha.diversity.genera.bytime <- genera.filt.div.df.meta %>%
  ggplot(aes(x = Time_Point, y = shannon.genera.filt, fill = Diet_By_Time_Point)) +
  geom_boxplot(outlier.shape = NA) +
  geom_point(aes(fill = Diet_By_Time_Point), color = "black", alpha = 0.7, position=position_jitterdodge()) +
  scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  scale_color_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, color = "black")) +
  labs(x=NULL, 
       y="Shannon diversity index", 
       title = "Alpha Diversity",
       subtitle = "Shannon Index, Genera Level", 
       fill="Diet & Time Point")

alpha.diversity.genera.bytime

ggsave("Figures/AlphaDiversityGenera_ByTime_Boxplot.png", 
       plot = alpha.diversity.genera.bytime, 
       dpi = 800, 
       width = 7, 
       height = 5)

2.7.4 Statistics

Repeated measures ANOVA on Shannon alpha diversity

# must remove columns that aren't used in anova
head(genera.filt.div.df.meta) 

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	shannon.genera.filt
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	3.454094
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	3.724200
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14	3.405044
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	14	Tomato	Day 7	Tomato Day 7	3.639925
ShotgunWGS-ControlPig5GutMicrobiome-Day7	5	Control	Day 7	Control Day 7	3.526991
ShotgunWGS-TomatoPig18GutMicrobiome-Day7	18	Tomato	Day 7	Tomato Day 7	3.281356

genera.filt.div.df.meta.foranova <- genera.filt.div.df.meta[,-c(1,5)]

head(genera.filt.div.df.meta.foranova)

Pig	Diet	Time_Point	shannon.genera.filt
6	Control	Day 14	3.454094
8	Control	Day 0	3.724200
3	Control	Day 14	3.405044
14	Tomato	Day 7	3.639925
5	Control	Day 7	3.526991
18	Tomato	Day 7	3.281356

genera.filt.alphadiv.anova <- 
  anova_test(data = genera.filt.div.df.meta.foranova,
             formula = shannon.genera.filt ~ Diet*Time_Point + Error(Pig/Time_Point),
             dv = shannon.genera.filt, 
             wid = Pig, 
             within = Time_Point, 
             between = Diet)

get_anova_table(genera.filt.alphadiv.anova)

Effect	DFn	DFd	F	p	p<.05	ges
Diet	1	18	2.888	0.106		0.066
Time_Point	2	36	0.254	0.777		0.008
Diet:Time_Point	2	36	0.037	0.964		0.001

• Non-significant effect of diet (p = 0.106)
  
• Non-significant effect of timepoint (0.777)
  
• Non-significant interaction of diet:time point (p = 0.964).

Check for normality

shapiro.test(genera.filt.div.df.meta.foranova$shannon.genera.filt)

## 
##  Shapiro-Wilk normality test
## 
## data:  genera.filt.div.df.meta.foranova$shannon.genera.filt
## W = 0.97004, p-value = 0.1466

Normal.

No need for posthoc test since no model parameters are significant.

2.8 ALDEx2

Quick introduction to anatomy of the aldex function

The aldex function does every step - data transformation and statistics
variable.name <- aldex(reads.data, variables.vector, mc.samples=#, test=“t”/“kw”, effect=T/F)
reads.data - your reads/count data, unchanged
variables.vector - a vector of the variables corresponding to sample groups, in SAME order as sample names (and therefore columns)
mc.samples - here you tell the function how many Monte Carlo sampels to use with an integer (128 is typical)
test - which test do you want, t-test and wilcoxon, or anova-like and kruskal wallace? (will always do the parametric and non-parametric) t = t-test and wilcoxon kw = anova-like and kruskal wallace
effect - do you want to incude effect results in output?

Key to aldex outputs - taken directly from vignette

• we.ep - Expected P value of Welch’s t test
• we.eBH - Expected Benjamini-Hochberg corrected P value of Welch’s t test
  
• wi.ep - Expected P value of Wilcoxon rank test
• wi.eBH - Expected Benjamini-Hochberg corrected P value of Wilcoxon test
• kw.ep - Expected P value of Kruskal-Wallace test
• kw.eBH - Expected Benjamini-Hochberg corrected P value of Kruskal-Wallace test
• glm.ep - Expected P value of glm test
• glm.eBH - Expected Benjamini-Hochberg corrected P value of glm test
• rab.all - median clr value for all samples in the feature
• rab.win.NS - median clr value for the NS group of samples
• rab.win.S - median clr value for the S group of samples
• dif.btw - median difference in clr values between S and NS groups
• dif.win - median of the largest difference in clr values within S and NS groups
• effect - median effect size: diff.btw / max(diff.win) for all instances
• overlap - proportion of effect size that overlaps 0 (i.e. no effect)

ALDEx2 takes counts, not relative abundance.

We are using Benjamini Hochberg corrected pvalues, or we.eBH for t-tests (i.e., subsetting by time), and Benjamini-Hochberg corrected pvalues of the glm test glm.eBH for ANOVA tests (i.e., subsetting by diet)

Downloading ALDEx2

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

BiocManager::install("ALDEx2")

2.8.1 Wrangling

Since we use counts for ALDEx2, we need to filter our counts data to include only the genera we ended up using in our final analysis

# this is the data set filtered to remove inplausible phyla, but still includes genera with a lot of missing values 
Genus.Counts.Filt[1:10,1:10]

domain	phylum	class	order	family	genus	ShotgunWGS-ControlPig6GutMicrobiome-Day14	ShotgunWGS-ControlPig8GutMicrobiome-Day0	ShotgunWGS-ControlPig3GutMicrobiome-Day14	ShotgunWGS-TomatoPig14GutMicrobiome-Day7
Viruses	unclassified (derived from Viruses)	unclassified (derived from Viruses)	Caudovirales	Podoviridae	AHJD-like viruses	29	0	153	0
Bacteria	Firmicutes	Bacilli	Lactobacillales	Aerococcaceae	Abiotrophia	5067	5661	4117	1576
Eukaryota	unclassified (derived from Eukaryota)	unclassified (derived from Eukaryota)	unclassified (derived from Eukaryota)	unclassified (derived from Eukaryota)	Acanthamoeba	0	0	0	1
Bacteria	Cyanobacteria	unclassified (derived from Cyanobacteria)	unclassified (derived from Cyanobacteria)	unclassified (derived from Cyanobacteria)	Acaryochloris	271	416	267	131
Bacteria	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Acetivibrio	1988	2981	2071	1012
Bacteria	Proteobacteria	Alphaproteobacteria	Rhodospirillales	Acetobacteraceae	Acetobacter	66	86	60	41
Bacteria	Firmicutes	Clostridia	Halanaerobiales	Halobacteroidaceae	Acetohalobium	1036	1373	1015	399
Bacteria	Tenericutes	Mollicutes	Acholeplasmatales	Acholeplasmataceae	Acholeplasma	779	1269	817	391
Bacteria	Proteobacteria	Betaproteobacteria	Burkholderiales	Alcaligenaceae	Achromobacter	192	298	197	90
Bacteria	Firmicutes	Negativicutes	Selenomonadales	Acidaminococcaceae	Acidaminococcus	50181	39909	31994	11378

dim(Genus.Counts.Filt)

## [1] 895  66

# final genera list (after filtering for zeros)
final_genera[1:10,]

##  [1] "Abiotrophia"     "Acaryochloris"   "Acetivibrio"     "Acetobacter"    
##  [5] "Acetohalobium"   "Acholeplasma"    "Achromobacter"   "Acidaminococcus"
##  [9] "Acidilobus"      "Acidimicrobium"

# how many final genera do we have?
dim(final_genera)

## [1] 755   1

# join to create a df with genera in rows, samples in columns
# filtered for genera used in this analysis
genera_counts_foraldex <- inner_join(final_genera, Genus.Counts.Filt,
                                     by = "genus")

dim(genera_counts_foraldex)

## [1] 755  66

# remove non-necessary metadata
genera_counts_foraldex <- genera_counts_foraldex[,-c(2:6)]

genera_counts_foraldex[1:10, 1:4]

genus	ShotgunWGS-ControlPig6GutMicrobiome-Day14	ShotgunWGS-ControlPig8GutMicrobiome-Day0	ShotgunWGS-ControlPig3GutMicrobiome-Day14
Abiotrophia	5067	5661	4117
Acaryochloris	271	416	267
Acetivibrio	1988	2981	2071
Acetobacter	66	86	60
Acetohalobium	1036	1373	1015
Acholeplasma	779	1269	817
Achromobacter	192	298	197
Acidaminococcus	50181	39909	31994
Acidilobus	10	20	25
Acidimicrobium	59	126	81

# add genera as rownames
rownames(genera_counts_foraldex) <- genera_counts_foraldex$genus

# remove genera as column for cleaner data
genera_counts_foraldex <- genera_counts_foraldex %>%
  select(-genus)

2.8.2 Subset by Time

2.8.2.1 Day 0

Look at the effect of diet at day 0.

# subset day 0 only
Day0.Counts.Genera.filt <- genera_counts_foraldex %>% 
  select(ends_with("Day0"))

ALDEx2 function needs a factor of variables

# order alphabetically so making the meta data vector is easier
Day0.Counts.Genera.filt <- Day0.Counts.Genera.filt[order(colnames(Day0.Counts.Genera.filt))]

Diets.Day0.Genera <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day0.Genera

##  [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
##  [8] "Control" "Control" "Control" "Tomato"  "Tomato"  "Tomato"  "Tomato" 
## [15] "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"

Run t-test

filt.Genera.Day0.ByDiet.aldex <- aldex(Day0.Counts.Genera.filt, 
                                       Diets.Day0.Genera, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)

## aldex.clr: generating Monte-Carlo instances and clr values

## operating in serial mode

## computing center with all features

## aldex.ttest: doing t-test

## aldex.effect: calculating effect sizes

filt.Genera.Day0.ByDiet.aldex <- 
  filt.Genera.Day0.ByDiet.aldex[order(filt.Genera.Day0.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Genera.Day0.ByDiet.aldex))

	rab.all	rab.win.Control	rab.win.Tomato	diff.btw	diff.win	effect	overlap	we.ep	we.eBH	wi.ep	wi.eBH
Methanoplanus	0.3240954	0.4568962	0.1596179	-0.2724204	0.2886546	-0.8660188	0.1797640	0.0158684	0.6555903	0.0208945	0.6397035
Herbaspirillum	-0.5526990	-0.7811902	-0.3502306	0.4911692	0.5776492	0.8366525	0.1697660	0.0108808	0.6588691	0.0156157	0.6379008
Caenorhabditis	-0.8691820	-0.7189714	-1.0364925	-0.3404582	0.4350202	-0.7586079	0.1829634	0.0223751	0.6615307	0.0306198	0.6405247
Gallionella	-0.8172239	-0.9796062	-0.6522070	0.3408789	0.4304651	0.7259556	0.1765647	0.0223797	0.6652116	0.0248262	0.6360060
Aurantimonas	-1.3040011	-1.1718677	-1.4419648	-0.3074244	0.4256663	-0.7048829	0.2107578	0.0369304	0.6652986	0.0616483	0.6579861
Collinsella	6.4066387	5.9053498	6.8799490	0.9085638	0.8861166	0.9100268	0.1924000	0.0098590	0.6660582	0.0206669	0.6547371

Create a histogram of pvalues of we.eBH

hist(filt.Genera.Day0.ByDiet.aldex$we.eBH,
     breaks = 20,
     main = "Histogram of p-values on the effect of diet at day 0 on genera",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")

we.eBH is the Benjamini-Hochberg corrected p-value, no significantly different genera at day 0.

2.8.2.2 Day 7

Look at the effect of diet at day 7.

# subset day 7 only
Day7.Counts.Genera.filt <- genera_counts_foraldex %>% 
  select(ends_with("Day7"))

ALDEx2 function needs a factor of variables

# order alphabetically so making the meta data vector is easier
Day7.Counts.Genera.filt <- Day7.Counts.Genera.filt[order(colnames(Day7.Counts.Genera.filt))]

Diets.Day7.Genera <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day7.Genera

##  [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
##  [8] "Control" "Control" "Control" "Tomato"  "Tomato"  "Tomato"  "Tomato" 
## [15] "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"

Run t-test

filt.Genera.Day7.ByDiet.aldex <- aldex(Day7.Counts.Genera.filt, 
                                       Diets.Day7.Genera, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)

## aldex.clr: generating Monte-Carlo instances and clr values

## operating in serial mode

## computing center with all features

## aldex.ttest: doing t-test

## aldex.effect: calculating effect sizes

filt.Genera.Day7.ByDiet.aldex <- 
  filt.Genera.Day7.ByDiet.aldex[order(filt.Genera.Day7.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Genera.Day7.ByDiet.aldex))

	rab.all	rab.win.Control	rab.win.Tomato	diff.btw	diff.win	effect	overlap	we.ep	we.eBH	wi.ep	wi.eBH
unclassified (derived from Bacteria)	2.5057815	1.9937104	3.0144580	0.8839157	0.5412860	1.6656644	0.0434000	0.0000329	0.0245673	0.0001843	0.0815681
Staphylococcus	4.3960776	4.2390261	4.6641527	0.3946606	0.2846407	1.3625038	0.0500000	0.0003371	0.1055372	0.0002435	0.0892256
Alphatorquevirus	-2.7756215	-3.9727969	-2.1861948	1.8893994	1.5061486	1.2108151	0.0945811	0.0013043	0.2031217	0.0016737	0.2269031
Lambda-like viruses	-0.5404410	-1.3459406	0.2594554	1.6046242	1.1806053	1.2088312	0.0979804	0.0013452	0.2268553	0.0016585	0.2418168
Clavibacter	0.7735975	0.5532127	1.0430190	0.6760412	0.6753707	0.9824101	0.1383723	0.0035910	0.3742773	0.0065620	0.4394420
Kluyveromyces	-2.7505518	-3.1253583	-2.3036482	0.8697441	0.9460260	0.8579568	0.1579684	0.0149994	0.5247221	0.0203586	0.5158019

One genera was significantly different by diet at day 7 - unclassified (derived from bacteria), padj = 0.025

hist(filt.Genera.Day7.ByDiet.aldex$we.eBH,
     breaks = 20,
     main = "Histogram of p-values on the effect of diet at day 7 on genera",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")

What is the directionality of the change?

filt.Genera.Day7.ByDiet.aldex %>%
  select(rab.win.Control, rab.win.Tomato, we.eBH) %>%
  filter(we.eBH <= 0.05)

	rab.win.Control	rab.win.Tomato	we.eBH
unclassified (derived from Bacteria)	1.99371	3.014458	0.0245673

Unclassified (derived from Bacteria) is higher in Tomato than Control.

2.8.2.3 Day 14

Look at the effect of diet on day 14.

# subset day 14 only
Day14.Counts.Genera.filt <- genera_counts_foraldex %>% 
  select(ends_with("Day14"))

ALDEx2 function needs a factor of variables

# order alphabetically so making the meta data vector is easier
Day14.Counts.Genera.filt <- Day14.Counts.Genera.filt[order(colnames(Day14.Counts.Genera.filt))]

Diets.Day14.Genera <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day14.Genera

##  [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
##  [8] "Control" "Control" "Control" "Tomato"  "Tomato"  "Tomato"  "Tomato" 
## [15] "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"

Run t-test

filt.Genera.Day14.ByDiet.aldex <- aldex(Day14.Counts.Genera.filt, 
                                       Diets.Day14.Genera, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)

## aldex.clr: generating Monte-Carlo instances and clr values

## operating in serial mode

## computing center with all features

## aldex.ttest: doing t-test

## aldex.effect: calculating effect sizes

filt.Genera.Day14.ByDiet.aldex <- 
  filt.Genera.Day14.ByDiet.aldex[order(filt.Genera.Day14.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Genera.Day14.ByDiet.aldex))

	rab.all	rab.win.Control	rab.win.Tomato	diff.btw	diff.win	effect	overlap	we.ep	we.eBH	wi.ep	wi.eBH
Lambda-like viruses	-0.6352044	-2.010975	1.1138858	3.1347459	0.5028891	6.132835	0.000014	0.00e+00	0.0000000	0.0000108	0.0017450
Staphylococcus	4.4895472	4.202858	4.8282810	0.6639986	0.2080582	3.307978	0.000014	0.00e+00	0.0000021	0.0000108	0.0017450
Alphatorquevirus	-2.8299641	-4.770810	-0.9945471	3.7552934	1.1350624	3.267378	0.000014	1.00e-07	0.0000156	0.0000108	0.0017457
unclassified (derived from Bacteria)	2.1224549	1.516367	2.9091701	1.3774322	0.5598447	2.490106	0.000014	2.00e-07	0.0000437	0.0000108	0.0017450
Loa	-3.2416301	-4.572084	-2.2187741	2.3637387	1.2681231	1.800528	0.024400	5.57e-05	0.0049752	0.0000992	0.0074401
Plasmodium	-0.4605634	-1.015288	-0.0936680	0.9135816	0.4657979	1.804893	0.056400	9.03e-05	0.0071475	0.0004384	0.0196350

hist(filt.Genera.Day14.ByDiet.aldex$we.eBH,
     breaks = 20,
     main = "Histogram of p-values on the effect of diet at day 14 on genera",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")

How many significant genera are there?

filt.Day14.Genera.aldex.sig <- filt.Genera.Day14.ByDiet.aldex[which(filt.Genera.Day14.ByDiet.aldex$we.eBH<0.05),]

length(rownames(filt.Day14.Genera.aldex.sig))

## [1] 14

Which genera are they?

sig_day14_genera_aldex2 <- as.data.frame(cbind(rownames(filt.Day14.Genera.aldex.sig),
                                 filt.Day14.Genera.aldex.sig$we.eBH))

sig_day14_genera_aldex2 <- sig_day14_genera_aldex2 %>%
  rename(Genera = V1,
         we.eBH_pvalue = V2)

sig_day14_genera_aldex2

Genera	we.eBH_pvalue
Lambda-like viruses	4.96308960966899e-08
Staphylococcus	2.07704872585305e-06
Alphatorquevirus	1.55575117192859e-05
unclassified (derived from Bacteria)	4.36786833982842e-05
Loa	0.00497522516224107
Plasmodium	0.00714748147881913
Propionibacterium	0.00888796295047635
Saccharomyces	0.0165200822142531
Stenotrophomonas	0.0216304981495325
Malassezia	0.0220427656046764
Roseiflexus	0.0228685343845818
Brugia	0.0311196253922183
Streptococcus	0.0316525514380945
Vanderwaltozyma	0.0371955835260783

• Lambda-like viruses
• Staphylococcus
• Alphatorquervirus
• unclassified (derived from Bacteria)
• Loa
• Plasmodium
• Propionibacterium
• Saccharomyces, Stenotrophomonas
• Malassezia
• Roseiflexus
• Brugia
• Strepococcus
• Vanderwaltozyma

What is the directionality of the change?

filt.Genera.Day14.ByDiet.aldex %>%
  select(rab.win.Control, rab.win.Tomato, we.eBH) %>%
  filter(we.eBH <= 0.05)

	rab.win.Control	rab.win.Tomato	we.eBH
Lambda-like viruses	-2.0109754	1.1138858	0.0000000
Staphylococcus	4.2028581	4.8282810	0.0000021
Alphatorquevirus	-4.7708100	-0.9945471	0.0000156
unclassified (derived from Bacteria)	1.5163672	2.9091701	0.0000437
Loa	-4.5720842	-2.2187741	0.0049752
Plasmodium	-1.0152880	-0.0936680	0.0071475
Propionibacterium	1.4089486	2.0243280	0.0088880
Saccharomyces	-1.9209051	0.3983332	0.0165201
Stenotrophomonas	0.0135488	0.4753067	0.0216305
Malassezia	-3.6221435	-1.7896205	0.0220428
Roseiflexus	2.6101603	2.3157595	0.0228685
Brugia	-3.1689702	-1.6127545	0.0311196
Streptococcus	10.5429359	8.0184005	0.0316526
Vanderwaltozyma	-4.0832524	-1.1614962	0.0371956

All significantly different genera are higher in tomato as compared to control.

2.8.3 Subset by diet

2.8.3.1 Control

# subset control only samples across all time points, should be n=30
Control.Counts.Genera.filt <- genera_counts_foraldex %>% 
  select(contains("Control"))

dim(Control.Counts.Genera.filt)

## [1] 755  30

ALDEx2 function needs a factor of variables

# results in pigs at different time points being grouped together
Control.Counts.Genera.filt <- Control.Counts.Genera.filt[order(colnames(Control.Counts.Genera.filt))]

# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc
TimePoints.Control.Genera <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))

# check and make sure it looks right
TimePoints.Control.Genera

##  [1] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [10] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [19] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [28] "Day0"  "Day14" "Day7"

More than two conditions this time, use the ANOVA-like test, Kruskal Wallis

filt.Genera.Control.ByTime.aldex <- aldex(Control.Counts.Genera.filt, 
                                          TimePoints.Control.Genera, 
                                          mc.samples = 1000, 
                                          test = "kw", 
                                          effect = FALSE)

## aldex.clr: generating Monte-Carlo instances and clr values

## operating in serial mode

## computing center with all features

## aldex.glm: doing Kruskal-Wallace and glm test (ANOVA-like)

## operating in serial mode

We are looking at glm.eBH for the BH corrected ANOVA pvalue

filt.Genera.Control.ByTime.aldex <- 
  filt.Genera.Control.ByTime.aldex[order(filt.Genera.Control.ByTime.aldex$glm.eBH, 
                                         decreasing = FALSE),]

kable(head(filt.Genera.Control.ByTime.aldex))

	kw.ep	kw.eBH	glm.ep	glm.eBH
Oribacterium	0.0005456	0.1122202	0.0000000	0.0000160
Streptococcus	0.0000759	0.0558493	0.0000000	0.0000161
Lactococcus	0.0004171	0.1049940	0.0000067	0.0016411
Granulicatella	0.0006810	0.1250608	0.0000994	0.0153695
T4-like viruses	0.0054139	0.3082631	0.0011363	0.0809611
Schizosaccharomyces	0.0089802	0.3728948	0.0026234	0.1274583

hist(filt.Genera.Control.ByTime.aldex$glm.eBH,
     breaks = 20,
     main = "Histogram of p-values on the effect of time within the control diet on genera",
     xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")

How many significantly different genera are there?

filt.Genera.Control.ByTime.aldex.sig <- 
  filt.Genera.Control.ByTime.aldex[which(filt.Genera.Control.ByTime.aldex$glm.eBH<0.05),]

length(rownames(filt.Genera.Control.ByTime.aldex.sig))

## [1] 4

4 sig genera

Which genera are they?

sig_control_genera_aldex2 <- as.data.frame(cbind(rownames(filt.Genera.Control.ByTime.aldex.sig),
                                 filt.Genera.Control.ByTime.aldex.sig$glm.eBH))

sig_control_genera_aldex2 <- sig_control_genera_aldex2 %>%
  rename(Genera = V1,
         glm.eBH_pval = V2)

sig_control_genera_aldex2

Genera	glm.eBH_pval
Oribacterium	1.59822166970723e-05
Streptococcus	1.60748583920067e-05
Lactococcus	0.00164110875408665
Granulicatella	0.015369528943084

• Oribacterium
• Streptococcus
• Lactococcus
• Granulicatella

2.8.3.2 Tomato

# subset tomato only samples across all time points, should be n=30
Tomato.Counts.Genera.filt <- genera_counts_foraldex %>% 
  select(contains("Tomato"))

ALDEx2 function needs a factor of variables

# results in pigs at different time points being grouped together
Tomato.Counts.Genera.filt <- Tomato.Counts.Genera.filt[order(colnames(Tomato.Counts.Genera.filt))]

# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc
TimePoints.Tomato.Genera <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))

# check and make sure it looks right
TimePoints.Tomato.Genera

##  [1] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [10] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [19] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [28] "Day0"  "Day14" "Day7"

More than two conditions this time, use the ANOVA-like test

filt.Genera.Tomato.ByTime.aldex <- aldex(Tomato.Counts.Genera.filt, 
                                          TimePoints.Tomato.Genera, 
                                          mc.samples = 1000, 
                                          test = "kw", 
                                          effect = FALSE)

## aldex.clr: generating Monte-Carlo instances and clr values

## operating in serial mode

## computing center with all features

## aldex.glm: doing Kruskal-Wallace and glm test (ANOVA-like)

## operating in serial mode

We are looking at glm.eBH for the BH corrected ANOVA pvalue

filt.Genera.Tomato.ByTime.aldex <- 
  filt.Genera.Tomato.ByTime.aldex[order(filt.Genera.Tomato.ByTime.aldex$glm.eBH, 
                                         decreasing = FALSE),]

kable(head(filt.Genera.Tomato.ByTime.aldex))

	kw.ep	kw.eBH	glm.ep	glm.eBH
Staphylococcus	0.0000567	0.0340761	0.0000000	0.0000001
Alphatorquevirus	0.0001163	0.0393049	0.0000003	0.0000809
Lambda-like viruses	0.0002251	0.0575195	0.0000016	0.0003898
unclassified (derived from Bacteria)	0.0019255	0.2943973	0.0000067	0.0012594
Streptococcus	0.0025578	0.3253182	0.0014963	0.1715984
Crocosphaera	0.0117944	0.4726381	0.0105064	0.3070718

hist(filt.Genera.Tomato.ByTime.aldex$glm.eBH,
     breaks = 20,
     main = "Histogram of p-values on the effect of time within the tomato diet on genera",
     xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")

How many significantly different genera are there?

filt.Genera.Tomato.ByTime.aldex.sig <- 
  filt.Genera.Tomato.ByTime.aldex[which(filt.Genera.Tomato.ByTime.aldex$glm.eBH<0.05),]

length(rownames(filt.Genera.Tomato.ByTime.aldex.sig))

## [1] 4

4 sig genera

Which genera are they?

sig_tomato_genera_aldex2 <- as.data.frame(cbind(rownames(filt.Genera.Tomato.ByTime.aldex.sig),
                                 filt.Genera.Tomato.ByTime.aldex.sig$glm.eBH))

sig_tomato_genera_aldex2 <- sig_tomato_genera_aldex2 %>%
  rename(Genera = V1,
         glm.eBH_pval = V2)

sig_tomato_genera_aldex2

Genera	glm.eBH_pval
Staphylococcus	1.12985069953709e-07
Alphatorquevirus	8.08890872003199e-05
Lambda-like viruses	0.000389807715045819
unclassified (derived from Bacteria)	0.00125944674946542

• Staphylococcus
• Alphatorquevirus
• Lambda-like viruses
• unclassified (derived from Bacteria)

Any overlap between sig differences at day 14 and by diet?

Control over time and day 14 overlap

intersect(sig_day14_genera_aldex2$Genera, sig_control_genera_aldex2$Genera)

## [1] "Streptococcus"

Streptococcus

Tomato over time and day 14 overlap

intersect(sig_day14_genera_aldex2$Genera, sig_tomato_genera_aldex2$Genera)

## [1] "Lambda-like viruses"                 
## [2] "Staphylococcus"                      
## [3] "Alphatorquevirus"                    
## [4] "unclassified (derived from Bacteria)"

• Lambda-like viruses
• Staphylococcus
• Alphatorquevirus
• unclassified (derived from Bacteria)

3 Phyla-level annotation

Read in phyla level data, annotated from MG-RAST. In “Phyla” tab of Supplementary Information.

Phyla.Counts <- read_excel("Goggans_etal_2021_tomato_pig_microbiome_WGS.xlsx",
                                       sheet = "Phyla")

str(Phyla.Counts)

## tibble [60 × 62] (S3: tbl_df/tbl/data.frame)
##  $ domain                                    : chr [1:60] "Bacteria" "Bacteria" "Eukaryota" "Bacteria" ...
##  $ phylum                                    : chr [1:60] "Acidobacteria" "Actinobacteria" "Apicomplexa" "Aquificae" ...
##  $ ShotgunWGS-ControlPig6GutMicrobiome-Day14 : num [1:60] 2874 186789 231 1953 368 ...
##  $ ShotgunWGS-ControlPig8GutMicrobiome-Day0  : num [1:60] 3717 277130 384 2254 992 ...
##  $ ShotgunWGS-ControlPig3GutMicrobiome-Day14 : num [1:60] 2663 126155 190 1642 386 ...
##  $ ShotgunWGS-TomatoPig14GutMicrobiome-Day7  : num [1:60] 880 39557 168 647 211 ...
##  $ ShotgunWGS-ControlPig5GutMicrobiome-Day7  : num [1:60] 2016 142345 171 1418 400 ...
##  $ ShotgunWGS-TomatoPig18GutMicrobiome-Day7  : num [1:60] 1377 181295 101 658 201 ...
##  $ ShotgunWGS-TomatoPig16GutMicrobiome-Day7  : num [1:60] 1570 58263 224 892 340 ...
##  $ ShotgunWGS-ControlPig10GutMicrobiome-Day7 : num [1:60] 1298 109273 287 900 211 ...
##  $ ShotgunWGS-ControlPig2GutMicrobiome-Day0  : num [1:60] 3114 159425 529 2095 764 ...
##  $ ShotgunWGS-TomatoPig18GutMicrobiome-Day0  : num [1:60] 2604 168472 170 1422 393 ...
##  $ ShotgunWGS-ControlPig10GutMicrobiome-Day0 : num [1:60] 3118 163425 231 1192 426 ...
##  $ ShotgunWGS-ControlPig7GutMicrobiome-Day0  : num [1:60] 2796 70967 389 1137 605 ...
##  $ ShotgunWGS-ControlPig8GutMicrobiome-Day14 : num [1:60] 2222 91465 325 1561 364 ...
##  $ ShotgunWGS-TomatoPig11GutMicrobiome-Day0  : num [1:60] 2136 68481 402 1377 691 ...
##  $ ShotgunWGS-TomatoPig19GutMicrobiome-Day0  : num [1:60] 2017 207693 143 1265 361 ...
##  $ ShotgunWGS-TomatoPig17GutMicrobiome-Day14 : num [1:60] 836 26050 147 633 212 ...
##  $ ShotgunWGS-ControlPig9GutMicrobiome-Day14 : num [1:60] 2612 172091 181 1645 393 ...
##  $ ShotgunWGS-ControlPig10GutMicrobiome-Day14: num [1:60] 2136 122681 250 1536 445 ...
##  $ ShotgunWGS-TomatoPig19GutMicrobiome-Day7  : num [1:60] 1090 78218 168 774 304 ...
##  $ ShotgunWGS-ControlPig5GutMicrobiome-Day14 : num [1:60] 2693 263950 266 1713 577 ...
##  $ ShotgunWGS-ControlPig2GutMicrobiome-Day7  : num [1:60] 3420 101192 582 2369 766 ...
##  $ ShotgunWGS-ControlPig6GutMicrobiome-Day7  : num [1:60] 2216 159323 115 1383 303 ...
##  $ ShotgunWGS-TomatoPig12GutMicrobiome-Day0  : num [1:60] 2146 78205 221 1265 390 ...
##  $ ShotgunWGS-TomatoPig14GutMicrobiome-Day0  : num [1:60] 732 77377 292 585 223 ...
##  $ ShotgunWGS-ControlPig7GutMicrobiome-Day14 : num [1:60] 2079 142139 322 1335 392 ...
##  $ ShotgunWGS-TomatoPig11GutMicrobiome-Day14 : num [1:60] 570 25927 180 425 270 ...
##  $ ShotgunWGS-TomatoPig20GutMicrobiome-Day0  : num [1:60] 2472 82091 415 1534 647 ...
##  $ ShotgunWGS-ControlPig9GutMicrobiome-Day0  : num [1:60] 1607 88397 432 1085 423 ...
##  $ ShotgunWGS-TomatoPig11GutMicrobiome-Day7  : num [1:60] 278 17451 96 150 107 ...
##  $ ShotgunWGS-TomatoPig13GutMicrobiome-Day7  : num [1:60] 1100 56205 157 984 306 ...
##  $ ShotgunWGS-TomatoPig17GutMicrobiome-Day0  : num [1:60] 1562 74553 171 780 238 ...
##  $ ShotgunWGS-TomatoPig19GutMicrobiome-Day14 : num [1:60] 765 47957 182 551 237 ...
##  $ ShotgunWGS-TomatoPig13GutMicrobiome-Day0  : num [1:60] 2182 124473 280 1483 476 ...
##  $ ShotgunWGS-ControlPig2GutMicrobiome-Day14 : num [1:60] 3329 116448 325 2149 703 ...
##  $ ShotgunWGS-ControlPig1GutMicrobiome-Day7  : num [1:60] 1920 55849 156 1234 408 ...
##  $ ShotgunWGS-TomatoPig15GutMicrobiome-Day7  : num [1:60] 757 38904 204 583 317 ...
##  $ ShotgunWGS-TomatoPig15GutMicrobiome-Day0  : num [1:60] 2037 120272 320 1399 561 ...
##  $ ShotgunWGS-TomatoPig12GutMicrobiome-Day7  : num [1:60] 1279 87121 215 917 409 ...
##  $ ShotgunWGS-TomatoPig14GutMicrobiome-Day14 : num [1:60] 583 36948 69 444 102 ...
##  $ ShotgunWGS-TomatoPig20GutMicrobiome-Day14 : num [1:60] 496 29179 99 374 142 ...
##  $ ShotgunWGS-ControlPig1GutMicrobiome-Day0  : num [1:60] 2963 90535 278 1596 631 ...
##  $ ShotgunWGS-ControlPig4GutMicrobiome-Day14 : num [1:60] 2548 133556 181 1734 432 ...
##  $ ShotgunWGS-ControlPig6GutMicrobiome-Day0  : num [1:60] 2269 127508 314 1058 413 ...
##  $ ShotgunWGS-TomatoPig16GutMicrobiome-Day0  : num [1:60] 1935 110140 207 1018 425 ...
##  $ ShotgunWGS-TomatoPig16GutMicrobiome-Day14 : num [1:60] 817 33981 133 443 187 ...
##  $ ShotgunWGS-TomatoPig18GutMicrobiome-Day14 : num [1:60] 705 92977 122 507 148 ...
##  $ ShotgunWGS-ControlPig7GutMicrobiome-Day7  : num [1:60] 1131 69602 297 628 290 ...
##  $ ShotgunWGS-ControlPig4GutMicrobiome-Day7  : num [1:60] 1298 112714 203 983 325 ...
##  $ ShotgunWGS-TomatoPig13GutMicrobiome-Day14 : num [1:60] 566 36447 72 514 125 ...
##  $ ShotgunWGS-ControlPig8GutMicrobiome-Day7  : num [1:60] 2173 159187 378 1311 361 ...
##  $ ShotgunWGS-TomatoPig15GutMicrobiome-Day14 : num [1:60] 1186 49134 150 858 249 ...
##  $ ShotgunWGS-TomatoPig12GutMicrobiome-Day14 : num [1:60] 1122 64744 254 1030 427 ...
##  $ ShotgunWGS-TomatoPig20GutMicrobiome-Day7  : num [1:60] 1109 97728 149 670 211 ...
##  $ ShotgunWGS-ControlPig1GutMicrobiome-Day14 : num [1:60] 2350 83993 210 1719 446 ...
##  $ ShotgunWGS-ControlPig3GutMicrobiome-Day0  : num [1:60] 3314 428097 206 2366 519 ...
##  $ ShotgunWGS-ControlPig5GutMicrobiome-Day0  : num [1:60] 2998 242356 283 1895 758 ...
##  $ ShotgunWGS-ControlPig4GutMicrobiome-Day0  : num [1:60] 3042 223010 351 1777 685 ...
##  $ ShotgunWGS-ControlPig9GutMicrobiome-Day7  : num [1:60] 499 68424 784 329 171 ...
##  $ ShotgunWGS-ControlPig3GutMicrobiome-Day7  : num [1:60] 2620 340300 165 1993 484 ...
##  $ ShotgunWGS-TomatoPig17GutMicrobime-Day7   : num [1:60] 1340 71395 159 648 270 ...

3.1 Data filtering

3.1.1 Remove inplausible phyla

These phyla are not plausibly found in a rectal swab of a pig, and were incorrectly annotated, so we are removing them.

Phyla.Counts.Filt <- Phyla.Counts %>%
  filter(phylum != "Chordata" , phylum != "Arthropoda" , phylum != "Cnidaria" , 
         phylum != "Porifera" , phylum != "Echinodermata", phylum != "Streptophyta",
         phylum != "Platyhelminthes")

Transpose.

Phyla.Counts.Filt.t <- as.tibble(t(Phyla.Counts.Filt))

# make phyla colnames
colnames(Phyla.Counts.Filt.t) <- Phyla.Counts.Filt.t[2,]

# remove domain, phylum rows
Phyla.Counts.Filt.t <- Phyla.Counts.Filt.t[3:62,]

# convert character to numeric
Phyla.Counts.Filt.t <- as.data.frame(apply((Phyla.Counts.Filt.t), 2, as.numeric))

str(Phyla.Counts.Filt.t[,1:5])

## 'data.frame':    60 obs. of  5 variables:
##  $ Acidobacteria : num  2874 3717 2663 880 2016 ...
##  $ Actinobacteria: num  186789 277130 126155 39557 142345 ...
##  $ Apicomplexa   : num  231 384 190 168 171 101 224 287 529 170 ...
##  $ Aquificae     : num  1953 2254 1642 647 1418 ...
##  $ Ascomycota    : num  1491 2196 1281 672 1178 ...

# add back sample names as column
Phyla.Counts.Filt.t <- Phyla.Counts.Filt.t %>%
  mutate(Sample_Name = AllSamples.Metadata$Sample_Name)

# move Sample_Name to first column
Phyla.Counts.Filt.t <- Phyla.Counts.Filt.t %>%
  relocate(Sample_Name)

kable(head(Phyla.Counts.Filt.t))

Sample_Name	Acidobacteria	Actinobacteria	Apicomplexa	Aquificae	Ascomycota	Bacillariophyta	Bacteroidetes	Basidiomycota	Blastocladiomycota	Candidatus Poribacteria	Chlamydiae	Chlorobi	Chloroflexi	Chlorophyta	Chromerida	Chrysiogenetes	Chytridiomycota	Crenarchaeota	Cyanobacteria	Deferribacteres	Deinococcus-Thermus	Dictyoglomi	Elusimicrobia	Euglenida	Euryarchaeota	Fibrobacteres	Firmicutes	Fusobacteria	Gemmatimonadetes	Glomeromycota	Hemichordata	Korarchaeota	Lentisphaerae	Microsporidia	Nanoarchaeota	Nematoda	Nitrospirae	Phaeophyceae	Placozoa	Planctomycetes	Proteobacteria	Spirochaetes	Synergistetes	Tenericutes	Thaumarchaeota	Thermotogae	Verrucomicrobia	Xanthophyceae	unclassified (derived from Bacteria)	unclassified (derived from Eukaryota)	unclassified (derived from Fungi)	unclassified (derived from Viruses)	unclassified (derived from other sequences)
ShotgunWGS-ControlPig6GutMicrobiome-Day14	2874	186789	231	1953	1491	105	1424565	240	0	26	552	4889	7842	370	0	331	0	648	8838	1494	2481	1217	632	3	13175	4768	2059948	15350	211	0	26	75	765	49	4	178	551	0	68	1523	105309	11519	4453	2764	56	5014	3209	1	1197	1260	0	1546	48
ShotgunWGS-ControlPig8GutMicrobiome-Day0	3717	277130	384	2254	2196	184	1391417	405	0	49	829	6073	9612	571	0	416	0	902	13612	1994	3586	1554	1007	0	19176	6963	2223331	21242	340	0	18	97	1833	88	5	265	797	1	68	2632	154698	17463	7489	3731	80	7105	6282	1	1720	4189	1	2626	33
ShotgunWGS-ControlPig3GutMicrobiome-Day14	2663	126155	190	1642	1281	129	1260217	198	0	21	554	4469	7596	362	0	271	0	638	11276	1419	2321	1109	699	0	12790	4985	2266610	14356	241	0	16	52	774	56	4	146	563	2	31	1570	104879	10922	4148	2493	59	5052	3738	0	1148	1266	0	2003	62
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	880	39557	168	647	672	65	415935	114	0	17	223	1565	2849	184	0	114	2	328	3426	543	967	424	359	1	8299	1750	628580	5545	61	0	5	36	492	24	6	138	241	1	19	540	71783	5634	1870	1292	37	1998	1259	0	1161	662	6	1010	115
ShotgunWGS-ControlPig5GutMicrobiome-Day7	2016	142345	171	1418	1178	111	798569	182	1	22	505	3835	6249	388	0	273	0	652	10511	1166	2095	897	665	2	13289	4040	1919749	13260	211	0	8	66	1339	80	4	161	483	1	27	1674	114187	10757	4358	2309	55	4527	3442	1	1323	1369	0	1475	13
ShotgunWGS-TomatoPig18GutMicrobiome-Day7	1377	181295	101	658	799	64	690378	119	0	17	222	2198	3255	220	0	91	0	274	7460	445	1102	423	230	0	4970	2071	793943	5068	208	0	2	25	288	32	1	134	245	0	29	1096	69391	4728	1482	915	26	1710	2361	0	1431	518	0	1267	59

Calculate relative abundance, and bind back to metadata.

Phyla.Counts.Filt.t.wtotal <- Phyla.Counts.Filt.t %>%
  mutate(Total.Counts = rowSums(Phyla.Counts.Filt.t[,2:ncol(Phyla.Counts.Filt.t)]))

dim(Phyla.Counts.Filt.t.wtotal)

## [1] 60 55

# create rel abund df
RelAbund.Phyla.Filt <- Phyla.Counts.Filt.t.wtotal[,2:54]/Phyla.Counts.Filt.t.wtotal$Total.Counts

# add back metadata
RelAbund.Phyla.Filt <- bind_cols(AllSamples.Metadata, RelAbund.Phyla.Filt)

3.1.2 Counting missing data

# remove metadata
RelAbund.Phyla.Filt.nometadata <- RelAbund.Phyla.Filt %>%
  select_if(is.numeric) 

# create a list with the number of zeros for each genus
counting_zeros_phyla <- sapply(RelAbund.Phyla.Filt.nometadata, function(x){ (sum(x==0))})

# plot a histogram to look
counting_zeros_phyla_df <- as.data.frame(counting_zeros_phyla)

hist(counting_zeros_phyla_df$counting_zeros_phyla, 
     breaks = 61,
     main = "Histogram of Genera with Zero Relative Intensity",
     sub = "Starting at No Zeros",
     xlab = "Number of zero relative intensity values",
     ylab = "Frequency")

Big first bar is many phyla which have zero missing values.

# filter for any phyla with at least 1 missing value
counting_zeros_phyla_df_missingval <- counting_zeros_phyla_df %>%
  rownames_to_column(var = "rowname") %>%
  filter(counting_zeros_phyla > 0) %>%
  column_to_rownames(var = "rowname")

# how many genera have at least one missing value?
dim(counting_zeros_phyla_df_missingval)

## [1] 9 1

9 phyla have at least 1 missing value.

# histogram of number of zeros, starting at 1 zero
hist(counting_zeros_phyla_df_missingval$counting_zeros_phyla, 
     breaks = 60,
     main = "Histogram of Genera with Zero Relative Intensity",
     sub = "Starting at 1 Zero",
     xlab = "Number of zero relative intensity values",
     ylab = "Frequency")

# create table of number of phyla with more than 1 missing value
counting_zeros_phyla_df_missingval

	counting_zeros_phyla
Blastocladiomycota	55
Chromerida	48
Chytridiomycota	42
Euglenida	31
Glomeromycota	59
Nanoarchaeota	8
Phaeophyceae	35
Xanthophyceae	29
unclassified (derived from Fungi)	46

3.1.3 Filter for <33% missingness

This would mean 33% missing values in our dataset.

# removing phyla that have 20 or more zeros
counting_zeros_phyla_df_missing20ormore <- counting_zeros_phyla_df %>%
  rownames_to_column(var = "rowname") %>%
  filter(counting_zeros_phyla >= 20) %>%
  column_to_rownames(var = "rowname")

# how many phyla have 20 or more missing value?
dim(counting_zeros_phyla_df_missing20ormore)

## [1] 8 1

8 phyla have more than 20 missing values.

# make a character vector from the rownames of previous data frame containing the phyla we want to get rid of
phyla.20zeros <- c(rownames(counting_zeros_phyla_df_missing20ormore))

# use select function to select all columns EXCEPT the ones in the character vector, we want to remove those
# and add in metadata
RelAbund.Phyla.Filt.zerofilt <- RelAbund.Phyla.Filt %>%
  select(everything(), -all_of(phyla.20zeros))

# check dimensions to make sure it filtered correctly
dim(RelAbund.Phyla.Filt.zerofilt)

## [1] 60 50

# removed 8, like we expected

Our final dataset has 45 phyla (because 5 columns are metadata).

Write final dataset genus rel abund to csv

write_csv(RelAbund.Phyla.Filt.zerofilt,
          file = "Phyla_RelAbund_Final_Filtered_WithMetadata.csv")

3.2 Microbiome profile

See “Genera” section above for rarefaction curves and kronas plots

3.2.1 Wrangling

Wrangling to enable collection of some summary statistics about our microbiome profile.

Grab names of final phyla

# contains inplausible genera removed, but not removed for zeroes
dim(Phyla.Counts.Filt)

## [1] 53 62

Phyla.Counts.Filt[1:10, 1:5]

domain	phylum	ShotgunWGS-ControlPig6GutMicrobiome-Day14	ShotgunWGS-ControlPig8GutMicrobiome-Day0	ShotgunWGS-ControlPig3GutMicrobiome-Day14
Bacteria	Acidobacteria	2874	3717	2663
Bacteria	Actinobacteria	186789	277130	126155
Eukaryota	Apicomplexa	231	384	190
Bacteria	Aquificae	1953	2254	1642
Eukaryota	Ascomycota	1491	2196	1281
Eukaryota	Bacillariophyta	105	184	129
Bacteria	Bacteroidetes	1424565	1391417	1260217
Eukaryota	Basidiomycota	240	405	198
Eukaryota	Blastocladiomycota	0	0	0
Bacteria	Candidatus Poribacteria	26	49	21

# final filtered data
RelAbund.Phyla.Filt.zerofilt[1:10, 1:5]

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	14	Tomato	Day 7	Tomato Day 7
ShotgunWGS-ControlPig5GutMicrobiome-Day7	5	Control	Day 7	Control Day 7
ShotgunWGS-TomatoPig18GutMicrobiome-Day7	18	Tomato	Day 7	Tomato Day 7
ShotgunWGS-TomatoPig16GutMicrobiome-Day7	16	Tomato	Day 7	Tomato Day 7
ShotgunWGS-ControlPig10GutMicrobiome-Day7	10	Control	Day 7	Control Day 7
ShotgunWGS-ControlPig2GutMicrobiome-Day0	2	Control	Day 0	Control Day 0
ShotgunWGS-TomatoPig18GutMicrobiome-Day0	18	Tomato	Day 0	Tomato Day 0

dim(RelAbund.Phyla.Filt.zerofilt)

## [1] 60 50

# grab colnames which have all the final phyla
final_phyla <- colnames(RelAbund.Phyla.Filt.zerofilt)

final_phyla

##  [1] "Sample_Name"                                
##  [2] "Pig"                                        
##  [3] "Diet"                                       
##  [4] "Time_Point"                                 
##  [5] "Diet_By_Time_Point"                         
##  [6] "Acidobacteria"                              
##  [7] "Actinobacteria"                             
##  [8] "Apicomplexa"                                
##  [9] "Aquificae"                                  
## [10] "Ascomycota"                                 
## [11] "Bacillariophyta"                            
## [12] "Bacteroidetes"                              
## [13] "Basidiomycota"                              
## [14] "Candidatus Poribacteria"                    
## [15] "Chlamydiae"                                 
## [16] "Chlorobi"                                   
## [17] "Chloroflexi"                                
## [18] "Chlorophyta"                                
## [19] "Chrysiogenetes"                             
## [20] "Crenarchaeota"                              
## [21] "Cyanobacteria"                              
## [22] "Deferribacteres"                            
## [23] "Deinococcus-Thermus"                        
## [24] "Dictyoglomi"                                
## [25] "Elusimicrobia"                              
## [26] "Euryarchaeota"                              
## [27] "Fibrobacteres"                              
## [28] "Firmicutes"                                 
## [29] "Fusobacteria"                               
## [30] "Gemmatimonadetes"                           
## [31] "Hemichordata"                               
## [32] "Korarchaeota"                               
## [33] "Lentisphaerae"                              
## [34] "Microsporidia"                              
## [35] "Nanoarchaeota"                              
## [36] "Nematoda"                                   
## [37] "Nitrospirae"                                
## [38] "Placozoa"                                   
## [39] "Planctomycetes"                             
## [40] "Proteobacteria"                             
## [41] "Spirochaetes"                               
## [42] "Synergistetes"                              
## [43] "Tenericutes"                                
## [44] "Thaumarchaeota"                             
## [45] "Thermotogae"                                
## [46] "Verrucomicrobia"                            
## [47] "unclassified (derived from Bacteria)"       
## [48] "unclassified (derived from Eukaryota)"      
## [49] "unclassified (derived from Viruses)"        
## [50] "unclassified (derived from other sequences)"

# remove metadata colnames
final_phyla <- final_phyla[6:50]  

final_phyla <- as.data.frame(final_phyla)

final_phyla <- final_phyla %>%
  rename(phylum = final_phyla)

Get back domain and inner_join with final_phyla list

# pull from full dataset the domain and genus columns
Phyla.Counts.Filt.Domain.Phyla <- Phyla.Counts.Filt %>%
  select(domain, phylum)

head(Phyla.Counts.Filt.Domain.Phyla)

domain	phylum
Bacteria	Acidobacteria
Bacteria	Actinobacteria
Eukaryota	Apicomplexa
Bacteria	Aquificae
Eukaryota	Ascomycota
Eukaryota	Bacillariophyta

# want to join Genus.Counts.Filt.Domain.Genera with final_phyla
final_phyla_withdomain <- inner_join(final_phyla, Phyla.Counts.Filt.Domain.Phyla,
                                     by = "phylum")

3.2.2 Count phyla

final_phyla_withdomain %>%
  count()

n
45

final_phyla_withdomain %>%
  group_by(domain) %>%
  count()

domain	n
Archaea	5
Bacteria	28
Eukaryota	10
other sequences	1
Viruses	1

3.2.3 Most prevalent phyla

RelAbund.Phyla.Filt.zerofilt[1:5, 1:10]

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	Acidobacteria	Actinobacteria	Apicomplexa	Aquificae	Ascomycota
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	0.0007406	0.0481336	0.0000595	0.0005033	0.0003842
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	0.0008850	0.0659807	0.0000914	0.0005366	0.0005228
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14	0.0006897	0.0326727	0.0000492	0.0004253	0.0003318
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	14	Tomato	Day 7	Tomato Day 7	0.0007324	0.0329202	0.0001398	0.0005384	0.0005593
ShotgunWGS-ControlPig5GutMicrobiome-Day7	5	Control	Day 7	Control Day 7	0.0006564	0.0463444	0.0000557	0.0004617	0.0003835

phyla_means <- RelAbund.Phyla.Filt.zerofilt %>%
  summarize_if(is.numeric, mean)

phyla_means_t <- t(phyla_means)
phyla_means_t <- as.data.frame(phyla_means_t)

phyla_means_t %>%
  rename(rel_abund_phyla = V1) %>%
  arrange(-rel_abund_phyla)

	rel_abund_phyla
Firmicutes	0.5273546
Bacteroidetes	0.3544950
Actinobacteria	0.0466013
Proteobacteria	0.0385954
Fusobacteria	0.0043000
Euryarchaeota	0.0042803
Spirochaetes	0.0037867
Cyanobacteria	0.0029731
Chloroflexi	0.0021439
Fibrobacteres	0.0016474
Thermotogae	0.0014760
Synergistetes	0.0014292
Chlorobi	0.0013717
Verrucomicrobia	0.0011132
Tenericutes	0.0008326
unclassified (derived from Viruses)	0.0007714
Acidobacteria	0.0007492
Deinococcus-Thermus	0.0007206
unclassified (derived from Eukaryota)	0.0005727
unclassified (derived from Bacteria)	0.0005606
Ascomycota	0.0005225
Planctomycetes	0.0005022
Aquificae	0.0004911
Deferribacteres	0.0004032
Lentisphaerae	0.0003300
Chlamydiae	0.0003263
Dictyoglomi	0.0003130
Elusimicrobia	0.0002375
Crenarchaeota	0.0002007
Nitrospirae	0.0001657
Chlorophyta	0.0001319
Apicomplexa	0.0001228
Basidiomycota	0.0000885
Nematoda	0.0000828
Chrysiogenetes	0.0000811
Gemmatimonadetes	0.0000665
Bacillariophyta	0.0000394
unclassified (derived from other sequences)	0.0000278
Microsporidia	0.0000211
Thaumarchaeota	0.0000190
Korarchaeota	0.0000188
Placozoa	0.0000143
Candidatus Poribacteria	0.0000103
Hemichordata	0.0000049
Nanoarchaeota	0.0000016

The most prevalent phyla are Firmicutes (52.7% average abundance), Bacteroidetes (35.4%), Actinobacteria (4.7%), Proteobacteria (3.9%) and Fusobaceria (0.43%).

What percent of the reads are from Bacteria?

final_phyla_bacteriaonly <- final_phyla_withdomain %>%
  filter(domain == "Bacteria")

final_phyla_bacteriaonly <- final_phyla_bacteriaonly$phylum

# select columns corresponding to bacteria
RelAbund.Phyla.Filt.zerofilt.baconly <- RelAbund.Phyla.Filt.zerofilt %>%
  select(contains(final_phyla_bacteriaonly)) 

# create rowsums
RelAbund.Phyla.Filt.zerofilt.baconly <- RelAbund.Phyla.Filt.zerofilt.baconly %>%
  mutate(rowsums = rowSums(RelAbund.Phyla.Filt.zerofilt.baconly[])) 

mean(RelAbund.Phyla.Filt.zerofilt.baconly$rowsums)

## [1] 0.9930777

sd(RelAbund.Phyla.Filt.zerofilt.baconly$rowsums)

## [1] 0.002045929

3.3 PERMANOVA

3.3.1 All samples, full model

Repeated measures, using Pig as a block and set permutations using how()

# create factors
factors_time_diet_pig_phyla <- RelAbund.Phyla.Filt.zerofilt %>% 
  select(Time_Point, Diet, Pig)

# create permutations
perm_time_diet_pig_phyla <- how(nperm = 9999)
setBlocks(perm_time_diet_pig_phyla) <- with(factors_time_diet_pig_phyla, Pig)

# run permanova
AllData.Phyla.Filt.permanova <- adonis2(RelAbund.Phyla.Filt.zerofilt[,-c(1:5)]~Diet*Time_Point,
                                        data = factors_time_diet_pig_phyla,
                                        permutations = perm_time_diet_pig_phyla,
                                        method = "bray")

AllData.Phyla.Filt.permanova

	Df	SumOfSqs	R2	F	Pr(>F)
Diet	1	0.0076754	0.0265577	1.674646	0.0150
Time_Point	2	0.0284960	0.0985997	3.108691	0.0054
Diet:Time_Point	2	0.0053384	0.0184716	0.582380	0.4870
Residual	54	0.2474969	0.8563709	NA	NA
Total	59	0.2890066	1.0000000	NA	NA

• Diet: p = 0.0150, significant
  
• Time_Point: p = 0.0054, significant
  
• Diet*Time_Point: p = 0.4870, non-significant

3.3.2 Subset by diet

3.3.2.1 Control

Effect of control diet over time. Pig as block.

# filter data set for only control samples
control.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Diet == "Control")

# create factors
factors_control_pig_phyla <- control.RelAbund.Phyla.zerofilt %>% 
  select(Time_Point, Pig)

# create permutations
perm_control_pig_phyla <- how(nperm = 9999)
setBlocks(perm_control_pig_phyla) <- with(factors_control_pig_phyla, Pig)

# run PERMANOVA
Control.ByTime.Phyla.zerofilt.permanova <- adonis2(control.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
        data = factors_control_pig_phyla,
        permutations = perm_control_pig_phyla, 
        method = "bray")

Control.ByTime.Phyla.zerofilt.permanova

	Df	SumOfSqs	R2	F	Pr(>F)
Time_Point	2	0.0259435	0.1748621	2.860901	0.0072
Residual	27	0.1224219	0.8251379	NA	NA
Total	29	0.1483653	1.0000000	NA	NA

Significant effect of time (p = 0.0072) within control samples.

3.3.2.2 Tomato

Effect of tomato diet over time.

# filter data for only tomato samples
tomato.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Diet == "Tomato")

# create factors
factors_tomato_pig_phyla <- tomato.RelAbund.Phyla.zerofilt %>% 
  select(Time_Point, Pig)

# create permutations
perm_tomato_pig_phyla <- how(nperm = 9999)
setBlocks(perm_tomato_pig_phyla) <- with(factors_tomato_pig_phyla, Pig)

# run PERMANOVA
tomato.ByTime.Phyla.zerofilt.permanova <- adonis2(tomato.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
                                                  data = factors_tomato_pig_phyla,
                                                  permutations = perm_tomato_pig_phyla, 
                                                  method = "bray")

tomato.ByTime.Phyla.zerofilt.permanova

	Df	SumOfSqs	R2	F	Pr(>F)
Time_Point	2	0.0078909	0.0593455	0.8517087	0.3407
Residual	27	0.1250750	0.9406545	NA	NA
Total	29	0.1329659	1.0000000	NA	NA

Non-significant effect of time (p = 0.3407) within tomato samples.

3.3.3 Subset by time

3.3.3.1 Day 0 Only

Effect of diet at day 0.

# filter data set for only day 0 samples
d0.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Time_Point == "Day 0")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day0_phyla <- d0.RelAbund.Phyla.zerofilt %>% 
  select(Diet)

# create permutations
perm_day0_phyla <- how(nperm = 9999)

# run PERMANOVA
d0.Phyla.zerofilt.permanova <- adonis2(d0.RelAbund.Phyla.zerofilt[,-c(1:5)]~Diet,
                                       data = factors_day0_phyla,
                                       permutations = perm_day0_phyla, 
                                       method = "bray")

d0.Phyla.zerofilt.permanova

	Df	SumOfSqs	R2	F	Pr(>F)
Diet	1	0.0035150	0.04898	0.927046	0.376
Residual	18	0.0682488	0.95102	NA	NA
Total	19	0.0717638	1.00000	NA	NA

Non-significant effect of diet (p=0.376) at day 0.

3.3.3.2 Day 7 Only

Effect of diet at day 7.

# filter data set for only day 7 samples
d7.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Time_Point == "Day 7")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day7_phyla <- d7.RelAbund.Phyla.zerofilt %>% 
  select(Diet)

# create permutations
perm_day7_phyla <- how(nperm = 9999)

# run PERMANOVA
d7.Phyla.zerofilt.permanova <- adonis2(d7.RelAbund.Phyla.zerofilt[,-c(1:5)]~Diet,
                                       data = factors_day7_phyla,
                                       permutations = perm_day7_phyla, 
                                       method = "bray")

d7.Phyla.zerofilt.permanova

	Df	SumOfSqs	R2	F	Pr(>F)
Diet	1	0.005267	0.0420497	0.790118	0.4097
Residual	18	0.119990	0.9579503	NA	NA
Total	19	0.125257	1.0000000	NA	NA

Non-significant effect of diet (p=0.4097) at day 7.

3.3.3.3 Day 14 Only

Effect of diet at day 14.

# filter data set for only day 14 samples
d14.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Time_Point == "Day 14")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day14_phyla <- d14.RelAbund.Phyla.zerofilt %>% 
  select(Diet)

# create permutations
perm_day14_phyla <- how(nperm = 9999)

# run PERMANOVA
d14.Phyla.zerofilt.permanova <- adonis2(d14.RelAbund.Phyla.zerofilt[,-c(1:5)]~Diet,
                                       data = factors_day14_phyla,
                                       permutations = perm_day14_phyla, 
                                       method = "bray")

d14.Phyla.zerofilt.permanova

	Df	SumOfSqs	R2	F	Pr(>F)
Diet	1	0.0042318	0.0666528	1.285429	0.256
Residual	18	0.0592581	0.9333472	NA	NA
Total	19	0.0634899	1.0000000	NA	NA

Non-significant effect of diet (p=0.256) at day 14.

3.4 PCoA Beta Diversity

3.4.1 All samples

# calculate distances
phyla.filt.dist.zeros <- vegdist(RelAbund.Phyla.Filt.zerofilt[6:ncol(RelAbund.Phyla.Filt.zerofilt)], 
                                 method = "bray")

# do multi-dimensional scaling (the PCoA calculations) on those distances
scale.phyla.filt.zerofilt <- cmdscale(phyla.filt.dist.zeros, k=2)

# make into data frame and bind metadata
scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(scale.phyla.filt.zerofilt, AllSamples.Metadata))

# do PCoA again, but get eigen values
scale.phyla.filt.zerofilt.eig <- cmdscale(phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
eigs.phyla.filt.zerofilt <- (100*((scale.phyla.filt.zerofilt.eig$eig)/(sum(scale.phyla.filt.zerofilt.eig$eig))))

# round the converted eigenvalues
round.eigs.phyla.filt.zerofilt <- round(eigs.phyla.filt.zerofilt, 3)

Plot

PCoA_phyla_20zeros_allsamples <- scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Diet_By_Time_Point)) +
  geom_point(size = 3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black"))+
  labs(x=paste("PC1: ", round.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level") 

PCoA_phyla_20zeros_allsamples

ggsave("Figures/BetaDiversity_PCoA_Phyla_allsamples.png", 
       plot = PCoA_phyla_20zeros_allsamples, 
       dpi = 800, 
       width = 10, 
       height = 8)

3.4.1.1 Facet by time point

Re-level factors

scale.phyla.filt.zerofilt.df <- scale.phyla.filt.zerofilt.df %>% 
  mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))

PCoA_phyla_20zeros_facetbytime <- scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Diet_By_Time_Point)) +
  geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_point(size = 3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_bw() +
  theme(axis.text = element_text(color = "black"),
        strip.background =element_rect(fill="white"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  labs(x=paste("PC1: ", round.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Subset by Time Point") +
  facet_wrap(~Time_Point)

PCoA_phyla_20zeros_facetbytime

ggsave("Figures/BetaDiversity_PCoA_Phyla_FacetByTimePoint.png", 
       plot = PCoA_phyla_20zeros_facetbytime, 
       dpi = 800, 
       width = 10, 
       height = 6)

3.4.1.2 Facet by diet

PCoA_phyla_20zeros_facetbydiet <- scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Diet_By_Time_Point)) +
  geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_point(size = 3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_bw() +
  theme(axis.text = element_text(color = "black"),
        strip.background =element_rect(fill="white"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  labs(x=paste("PC1: ", round.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Subset by Diet") +
  facet_wrap(~Diet)

PCoA_phyla_20zeros_facetbydiet

ggsave("Figures/BetaDiversity_PCoA_Phyla_FacetByDiet.png", 
       plot = PCoA_phyla_20zeros_facetbydiet, 
       dpi = 800, 
       width = 10, 
       height = 8)

3.4.2 Subset

Ended up not using this as part of the paper. Since the input is different here (i.e., the PCoA only has the subset data as an input) the output looks slightly different.

3.4.2.1 Control only

# calculate distances
control.phyla.filt.dist.zeros <- vegdist(control.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")

# do PCoA calculations
control.scale.phyla.filt.zerofilt <- cmdscale(control.phyla.filt.dist.zeros, k=2)

# filter meta data
meta.control <- subset(AllSamples.Metadata, Diet == "Control")

# make pcoa table into data frame and bind metadata to it
control.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.control, control.scale.phyla.filt.zerofilt))

# do PCoA again, but get eigenvalues
control.scale.phyla.filt.zerofilt.eig <- cmdscale(control.phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
control.eigs.phyla.filt.zerofilt <- (100*((control.scale.phyla.filt.zerofilt.eig$eig)/sum(control.scale.phyla.filt.zerofilt.eig$eig)))

# round the eigenvalues
round.control.eigs.phyla.filt.zerofilt <- round(control.eigs.phyla.filt.zerofilt, 3)

Re-level factors

control.scale.phyla.filt.zerofilt.df$Time_Point <- factor(control.scale.phyla.filt.zerofilt.df$Time_Point, 
                                                          levels = c("Day 0", "Day 7", "Day 14"))

Plot

control.scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Time_Point)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=paste("PC1: ", round.control.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.control.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Control Only")

3.4.2.2 Tomato only

# calculate distances
tomato.phyla.filt.dist.zeros <- vegdist(tomato.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")

# do PCoA calculations
tomato.scale.phyla.filt.zerofilt <- cmdscale(tomato.phyla.filt.dist.zeros, k=2)

# filter meta data
meta.tomato <- subset(AllSamples.Metadata, Diet == "Tomato")

# make pcoa table into data frame and bind metadata to it
tomato.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.tomato, tomato.scale.phyla.filt.zerofilt))

# do PCoA again, but get eigenvalues
tomato.scale.phyla.filt.zerofilt.eig <- cmdscale(tomato.phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
tomato.eigs.phyla.filt.zerofilt <- (100*((tomato.scale.phyla.filt.zerofilt.eig$eig)/sum(tomato.scale.phyla.filt.zerofilt.eig$eig)))

# round the eigenvalues
round.tomato.eigs.phyla.filt.zerofilt <- round(tomato.eigs.phyla.filt.zerofilt, 3)

Re-level factors

tomato.scale.phyla.filt.zerofilt.df$Time_Point <- factor(tomato.scale.phyla.filt.zerofilt.df$Time_Point, 
                                                         levels = c("Day 0", "Day 7", "Day 14"))

Plot

tomato.scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Time_Point)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("sienna1","firebrick3","tomato4")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=paste("PC1: ", round.tomato.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.tomato.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Tomato Only")

3.4.2.3 Day 0 Only

# calculate distances
d0.phyla.filt.dist.zeros <- vegdist(d0.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")

# do PCoA calculations
d0.scale.phyla.filt.zerofilt <- cmdscale(d0.phyla.filt.dist.zeros, k=2)

# filter meta data
meta.d0 <- subset(AllSamples.Metadata, Time_Point == "Day 0")

# make pcoa table into data frame and bind metadata to it
d0.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.d0, d0.scale.phyla.filt.zerofilt))

# do PCoA again, but get eigenvalues
d0.scale.phyla.filt.zerofilt.eig <- cmdscale(d0.phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
d0.eigs.phyla.filt.zerofilt <- (100*((d0.scale.phyla.filt.zerofilt.eig$eig)/sum(d0.scale.phyla.filt.zerofilt.eig$eig)))

# round the eigenvalues
round.d0.eigs.phyla.filt.zerofilt <- round(d0.eigs.phyla.filt.zerofilt, 3)

Plot

d0.scale.phyla.filt.zerofilt.df %>%
  ggplot(aes(x = `1`, y = `2`, fill = Diet)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=paste("PC1: ", round.d0.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.d0.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Day 0 Only")

3.4.2.4 Day 7 Only

# calculate distances
d7.phyla.filt.dist.zeros <- vegdist(d7.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")

# do PCoA calculations
d7.scale.phyla.filt.zerofilt <- cmdscale(d7.phyla.filt.dist.zeros, k=2)

# filter meta data
meta.d7 <- subset(AllSamples.Metadata, Time_Point == "Day 7")

# make pcoa table into data frame and bind metadata to it
d7.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.d7, d7.scale.phyla.filt.zerofilt))

# do PCoA again, but get eigenvalues
d7.scale.phyla.filt.zerofilt.eig <- cmdscale(d7.phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
d7.eigs.phyla.filt.zerofilt <- (100*((d7.scale.phyla.filt.zerofilt.eig$eig)/sum(d7.scale.phyla.filt.zerofilt.eig$eig)))

# round the eigenvalues
round.d7.eigs.phyla.filt.zerofilt <- round(d7.eigs.phyla.filt.zerofilt, 3)

Plot

d7.scale.phyla.filt.zerofilt.df %>%
  ggplot(aes(x = `1`, y = `2`, fill = Diet)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=paste("PC1: ", round.d7.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.d7.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Day 7 Only")

3.4.2.5 Day 14 Only

# calculate distances
d14.phyla.filt.dist.zeros <- vegdist(d14.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")
# do PCoA calculations
d14.scale.phyla.filt.zerofilt <- cmdscale(d14.phyla.filt.dist.zeros, k=2)

# filter meta data
meta.d14 <- subset(AllSamples.Metadata, Time_Point == "Day 14")

# make pcoa table into data frame and bind metadata to it
d14.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.d14, d14.scale.phyla.filt.zerofilt))

# do PCoA again, but get eigenvalues
d14.scale.phyla.filt.zerofilt.eig <- cmdscale(d14.phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
d14.eigs.phyla.filt.zerofilt <- (100*((d14.scale.phyla.filt.zerofilt.eig$eig)/sum(d14.scale.phyla.filt.zerofilt.eig$eig)))

# round the eigenvalues
round.d14.eigs.phyla.filt.zerofilt <- round(d14.eigs.phyla.filt.zerofilt, 3)

Plot

d14.scale.phyla.filt.zerofilt.df %>%
  ggplot(aes(x = `1`, y = `2`, fill = Diet)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=paste("PC1: ", round.d14.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.d14.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Day 14 Only")

3.5 Bacteriodetes, Firmicutes, and B to F

Given a priori interest in the phyla Bacteriodetes and Firmicutes, we are conducted repeated measures ANOVA analysis for their changes in our samples. The ratio of Bacteriodetes to Firmicutes (B to F) is a commonly used metric for assessing the health of the microbiome, with a higher B to F being more beneficial.

3.5.1 Wrangling

dim(RelAbund.Phyla.Filt.zerofilt)

## [1] 60 50

60 samples, and 45 phyla (5 columns are metadata).

Re-level Time_Point

RelAbund.Phyla.Filt.zerofilt <- RelAbund.Phyla.Filt.zerofilt %>%
  mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))

levels(RelAbund.Phyla.Filt.zerofilt$Time_Point)

## [1] "Day 0"  "Day 7"  "Day 14"

Add column Other_phyla with the sum of all phyla that are not Bacteroidetes or Firmicutes

RelAbund.Phyla.Filt.zerofilt.withother <- RelAbund.Phyla.Filt.zerofilt %>%
  mutate(Other_phyla = rowSums(select(.[6:ncol(.)], !contains(c("Bacteroidetes", "Firmicutes")))))

kable(head(RelAbund.Phyla.Filt.zerofilt.withother))

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	Acidobacteria	Actinobacteria	Apicomplexa	Aquificae	Ascomycota	Bacillariophyta	Bacteroidetes	Basidiomycota	Candidatus Poribacteria	Chlamydiae	Chlorobi	Chloroflexi	Chlorophyta	Chrysiogenetes	Crenarchaeota	Cyanobacteria	Deferribacteres	Deinococcus-Thermus	Dictyoglomi	Elusimicrobia	Euryarchaeota	Fibrobacteres	Firmicutes	Fusobacteria	Gemmatimonadetes	Hemichordata	Korarchaeota	Lentisphaerae	Microsporidia	Nanoarchaeota	Nematoda	Nitrospirae	Placozoa	Planctomycetes	Proteobacteria	Spirochaetes	Synergistetes	Tenericutes	Thaumarchaeota	Thermotogae	Verrucomicrobia	unclassified (derived from Bacteria)	unclassified (derived from Eukaryota)	unclassified (derived from Viruses)	unclassified (derived from other sequences)	Other_phyla
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	0.0007406	0.0481336	0.0000595	0.0005033	0.0003842	2.71e-05	0.3670956	6.18e-05	6.70e-06	0.0001422	0.0012598	0.0020208	0.0000953	8.53e-05	0.0001670	0.0022775	0.0003850	0.0006393	0.0003136	0.0001629	0.0033951	0.0012287	0.5308271	0.0039555	0.0000544	6.7e-06	1.93e-05	0.0001971	1.26e-05	1.0e-06	0.0000459	0.0001420	1.75e-05	0.0003925	0.0271370	0.0029683	0.0011475	0.0007123	1.44e-05	0.0012921	0.0008269	0.0003085	0.0003247	0.0003984	1.24e-05	0.1020763
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	0.0008850	0.0659807	0.0000914	0.0005366	0.0005228	4.38e-05	0.3312767	9.64e-05	1.17e-05	0.0001974	0.0014459	0.0022885	0.0001359	9.90e-05	0.0002148	0.0032408	0.0004747	0.0008538	0.0003700	0.0002398	0.0045655	0.0016578	0.5293436	0.0050574	0.0000809	4.3e-06	2.31e-05	0.0004364	2.10e-05	1.2e-06	0.0000631	0.0001898	1.62e-05	0.0006266	0.0368314	0.0041577	0.0017830	0.0008883	1.90e-05	0.0016916	0.0014957	0.0004095	0.0009973	0.0006252	7.90e-06	0.1393790
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14	0.0006897	0.0326727	0.0000492	0.0004253	0.0003318	3.34e-05	0.3263817	5.13e-05	5.40e-06	0.0001435	0.0011574	0.0019673	0.0000938	7.02e-05	0.0001652	0.0029204	0.0003675	0.0006011	0.0002872	0.0001810	0.0033125	0.0012911	0.5870258	0.0037180	0.0000624	4.1e-06	1.35e-05	0.0002005	1.45e-05	1.0e-06	0.0000378	0.0001458	8.00e-06	0.0004066	0.0271624	0.0028287	0.0010743	0.0006457	1.53e-05	0.0013084	0.0009681	0.0002973	0.0003279	0.0005188	1.61e-05	0.0865920
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	14	Tomato	Day 7	Tomato Day 7	0.0007324	0.0329202	0.0001398	0.0005384	0.0005593	5.41e-05	0.3461498	9.49e-05	1.41e-05	0.0001856	0.0013024	0.0023710	0.0001531	9.49e-05	0.0002730	0.0028512	0.0004519	0.0008048	0.0003529	0.0002988	0.0069066	0.0014564	0.5231174	0.0046147	0.0000508	4.2e-06	3.00e-05	0.0004095	2.00e-05	5.0e-06	0.0001148	0.0002006	1.58e-05	0.0004494	0.0597393	0.0046887	0.0015563	0.0010752	3.08e-05	0.0016628	0.0010478	0.0009662	0.0005509	0.0008405	9.57e-05	0.1307244
ShotgunWGS-ControlPig5GutMicrobiome-Day7	5	Control	Day 7	Control Day 7	0.0006564	0.0463444	0.0000557	0.0004617	0.0003835	3.61e-05	0.2599966	5.93e-05	7.20e-06	0.0001644	0.0012486	0.0020345	0.0001263	8.89e-05	0.0002123	0.0034222	0.0003796	0.0006821	0.0002920	0.0002165	0.0043266	0.0013153	0.6250284	0.0043172	0.0000687	2.6e-06	2.15e-05	0.0004359	2.60e-05	1.3e-06	0.0000524	0.0001573	8.80e-06	0.0005450	0.0371768	0.0035022	0.0014189	0.0007518	1.79e-05	0.0014739	0.0011206	0.0004307	0.0004457	0.0004802	4.20e-06	0.1149734
ShotgunWGS-TomatoPig18GutMicrobiome-Day7	18	Tomato	Day 7	Tomato Day 7	0.0007724	0.1016953	0.0000567	0.0003691	0.0004482	3.59e-05	0.3872593	6.68e-05	9.50e-06	0.0001245	0.0012329	0.0018259	0.0001234	5.10e-05	0.0001537	0.0041846	0.0002496	0.0006182	0.0002373	0.0001290	0.0027879	0.0011617	0.4453529	0.0028428	0.0001167	1.1e-06	1.40e-05	0.0001616	1.80e-05	6.0e-07	0.0000752	0.0001374	1.63e-05	0.0006148	0.0389241	0.0026521	0.0008313	0.0005133	1.46e-05	0.0009592	0.0013244	0.0008027	0.0002906	0.0007107	3.31e-05	0.1673878

Add column B to F

RelAbund.Phyla.Filt.zerofilt.withother.BtoF <- RelAbund.Phyla.Filt.zerofilt.withother %>%
  mutate(BtoF = Bacteroidetes/Firmicutes)

kable(head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF))

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	Acidobacteria	Actinobacteria	Apicomplexa	Aquificae	Ascomycota	Bacillariophyta	Bacteroidetes	Basidiomycota	Candidatus Poribacteria	Chlamydiae	Chlorobi	Chloroflexi	Chlorophyta	Chrysiogenetes	Crenarchaeota	Cyanobacteria	Deferribacteres	Deinococcus-Thermus	Dictyoglomi	Elusimicrobia	Euryarchaeota	Fibrobacteres	Firmicutes	Fusobacteria	Gemmatimonadetes	Hemichordata	Korarchaeota	Lentisphaerae	Microsporidia	Nanoarchaeota	Nematoda	Nitrospirae	Placozoa	Planctomycetes	Proteobacteria	Spirochaetes	Synergistetes	Tenericutes	Thaumarchaeota	Thermotogae	Verrucomicrobia	unclassified (derived from Bacteria)	unclassified (derived from Eukaryota)	unclassified (derived from Viruses)	unclassified (derived from other sequences)	Other_phyla	BtoF
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	0.0007406	0.0481336	0.0000595	0.0005033	0.0003842	2.71e-05	0.3670956	6.18e-05	6.70e-06	0.0001422	0.0012598	0.0020208	0.0000953	8.53e-05	0.0001670	0.0022775	0.0003850	0.0006393	0.0003136	0.0001629	0.0033951	0.0012287	0.5308271	0.0039555	0.0000544	6.7e-06	1.93e-05	0.0001971	1.26e-05	1.0e-06	0.0000459	0.0001420	1.75e-05	0.0003925	0.0271370	0.0029683	0.0011475	0.0007123	1.44e-05	0.0012921	0.0008269	0.0003085	0.0003247	0.0003984	1.24e-05	0.1020763	0.6915539
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	0.0008850	0.0659807	0.0000914	0.0005366	0.0005228	4.38e-05	0.3312767	9.64e-05	1.17e-05	0.0001974	0.0014459	0.0022885	0.0001359	9.90e-05	0.0002148	0.0032408	0.0004747	0.0008538	0.0003700	0.0002398	0.0045655	0.0016578	0.5293436	0.0050574	0.0000809	4.3e-06	2.31e-05	0.0004364	2.10e-05	1.2e-06	0.0000631	0.0001898	1.62e-05	0.0006266	0.0368314	0.0041577	0.0017830	0.0008883	1.90e-05	0.0016916	0.0014957	0.0004095	0.0009973	0.0006252	7.90e-06	0.1393790	0.6258254
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14	0.0006897	0.0326727	0.0000492	0.0004253	0.0003318	3.34e-05	0.3263817	5.13e-05	5.40e-06	0.0001435	0.0011574	0.0019673	0.0000938	7.02e-05	0.0001652	0.0029204	0.0003675	0.0006011	0.0002872	0.0001810	0.0033125	0.0012911	0.5870258	0.0037180	0.0000624	4.1e-06	1.35e-05	0.0002005	1.45e-05	1.0e-06	0.0000378	0.0001458	8.00e-06	0.0004066	0.0271624	0.0028287	0.0010743	0.0006457	1.53e-05	0.0013084	0.0009681	0.0002973	0.0003279	0.0005188	1.61e-05	0.0865920	0.5559920
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	14	Tomato	Day 7	Tomato Day 7	0.0007324	0.0329202	0.0001398	0.0005384	0.0005593	5.41e-05	0.3461498	9.49e-05	1.41e-05	0.0001856	0.0013024	0.0023710	0.0001531	9.49e-05	0.0002730	0.0028512	0.0004519	0.0008048	0.0003529	0.0002988	0.0069066	0.0014564	0.5231174	0.0046147	0.0000508	4.2e-06	3.00e-05	0.0004095	2.00e-05	5.0e-06	0.0001148	0.0002006	1.58e-05	0.0004494	0.0597393	0.0046887	0.0015563	0.0010752	3.08e-05	0.0016628	0.0010478	0.0009662	0.0005509	0.0008405	9.57e-05	0.1307244	0.6617057
ShotgunWGS-ControlPig5GutMicrobiome-Day7	5	Control	Day 7	Control Day 7	0.0006564	0.0463444	0.0000557	0.0004617	0.0003835	3.61e-05	0.2599966	5.93e-05	7.20e-06	0.0001644	0.0012486	0.0020345	0.0001263	8.89e-05	0.0002123	0.0034222	0.0003796	0.0006821	0.0002920	0.0002165	0.0043266	0.0013153	0.6250284	0.0043172	0.0000687	2.6e-06	2.15e-05	0.0004359	2.60e-05	1.3e-06	0.0000524	0.0001573	8.80e-06	0.0005450	0.0371768	0.0035022	0.0014189	0.0007518	1.79e-05	0.0014739	0.0011206	0.0004307	0.0004457	0.0004802	4.20e-06	0.1149734	0.4159757
ShotgunWGS-TomatoPig18GutMicrobiome-Day7	18	Tomato	Day 7	Tomato Day 7	0.0007724	0.1016953	0.0000567	0.0003691	0.0004482	3.59e-05	0.3872593	6.68e-05	9.50e-06	0.0001245	0.0012329	0.0018259	0.0001234	5.10e-05	0.0001537	0.0041846	0.0002496	0.0006182	0.0002373	0.0001290	0.0027879	0.0011617	0.4453529	0.0028428	0.0001167	1.1e-06	1.40e-05	0.0001616	1.80e-05	6.0e-07	0.0000752	0.0001374	1.63e-05	0.0006148	0.0389241	0.0026521	0.0008313	0.0005133	1.46e-05	0.0009592	0.0013244	0.0008027	0.0002906	0.0007107	3.31e-05	0.1673878	0.8695561

Convert data from wide to long (i.e. make data tidy)

RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
  pivot_longer(cols = 6:ncol(.),
               names_to = "phylum",
               values_to = "rel_abund")

RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long[1:10,]

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	phylum	rel_abund
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	Acidobacteria	0.0007406
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	Actinobacteria	0.0481336
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	Apicomplexa	0.0000595
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	Aquificae	0.0005033
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	Ascomycota	0.0003842
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	Bacillariophyta	0.0000271
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	Bacteroidetes	0.3670956
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	Basidiomycota	0.0000618
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	Candidatus Poribacteria	0.0000067
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	Chlamydiae	0.0001422

3.5.2 Plotting

Stacked bar chart of B, F, and all the other phyla

RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BFandOther <-
  RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long %>%
    filter(phylum %in% c("Bacteroidetes", "Firmicutes", "Other_phyla"))

head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BFandOther)

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	phylum	rel_abund
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	Bacteroidetes	0.3670956
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	Firmicutes	0.5308271
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	Other_phyla	0.1020763
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	Bacteroidetes	0.3312767
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	Firmicutes	0.5293436
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	Other_phyla	0.1393790

RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BFandOther %>%
  ggplot(aes(x=as.numeric(Pig), y=rel_abund, fill=phylum))+
  geom_col()+
  scale_fill_brewer(palette = "GnBu") +
  facet_grid(~Time_Point)+
  theme_classic()+
  labs(y="Relative Abundance", 
       fill="Phylum",
       x = "Pig") +
  theme(panel.grid = element_blank(), axis.text = element_text(color="black"),
        strip.text = element_text(color = "black", size = 14), 
        strip.background = element_blank())

3.5.3 B to F Ratio

3.5.3.1 Plotting

B to F boxplot with jitter

RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BtoF <- 
  RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long %>%
  filter(phylum == "BtoF")

head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BtoF)

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	phylum	rel_abund
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	BtoF	0.6915539
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	BtoF	0.6258254
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14	BtoF	0.5559920
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	14	Tomato	Day 7	Tomato Day 7	BtoF	0.6617057
ShotgunWGS-ControlPig5GutMicrobiome-Day7	5	Control	Day 7	Control Day 7	BtoF	0.4159757
ShotgunWGS-TomatoPig18GutMicrobiome-Day7	18	Tomato	Day 7	Tomato Day 7	BtoF	0.8695561

BtoF_Boxplot <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BtoF %>%
  ggplot(aes(x=Diet, y=rel_abund, fill=Diet_By_Time_Point))+
  geom_boxplot(outlier.shape = NA)+
  geom_point(aes(fill = Diet_By_Time_Point), color = "black", alpha = 0.7, position=position_jitterdodge()) +
  scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  scale_color_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  ylim(0, 1) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, color = "black"), 
        axis.text.y = element_text(color = "black"), 
        panel.grid.minor = element_blank()) +
  labs(x=NULL, 
       y= "Bacteroidetes to Firmicutes", 
       fill="Diet & Time Point",
       title = "Ratio of Bacteroidetes to Firmicutes") 

BtoF_Boxplot

## Warning: Removed 5 rows containing non-finite values (stat_boxplot).

## Warning: Removed 5 rows containing missing values (geom_point).

Saving

ggsave("Figures/BtoFRatio_Boxplot.png", 
       plot = BtoF_Boxplot, 
       dpi = 800, 
       width = 7, 
       height = 5)

3.5.3.2 Statistics

head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF)

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	Acidobacteria	Actinobacteria	Apicomplexa	Aquificae	Ascomycota	Bacillariophyta	Bacteroidetes	Basidiomycota	Candidatus Poribacteria	Chlamydiae	Chlorobi	Chloroflexi	Chlorophyta	Chrysiogenetes	Crenarchaeota	Cyanobacteria	Deferribacteres	Deinococcus-Thermus	Dictyoglomi	Elusimicrobia	Euryarchaeota	Fibrobacteres	Firmicutes	Fusobacteria	Gemmatimonadetes	Hemichordata	Korarchaeota	Lentisphaerae	Microsporidia	Nanoarchaeota	Nematoda	Nitrospirae	Placozoa	Planctomycetes	Proteobacteria	Spirochaetes	Synergistetes	Tenericutes	Thaumarchaeota	Thermotogae	Verrucomicrobia	unclassified (derived from Bacteria)	unclassified (derived from Eukaryota)	unclassified (derived from Viruses)	unclassified (derived from other sequences)	Other_phyla	BtoF
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	0.0007406	0.0481336	0.0000595	0.0005033	0.0003842	2.71e-05	0.3670956	6.18e-05	6.70e-06	0.0001422	0.0012598	0.0020208	0.0000953	8.53e-05	0.0001670	0.0022775	0.0003850	0.0006393	0.0003136	0.0001629	0.0033951	0.0012287	0.5308271	0.0039555	0.0000544	6.7e-06	1.93e-05	0.0001971	1.26e-05	1.0e-06	0.0000459	0.0001420	1.75e-05	0.0003925	0.0271370	0.0029683	0.0011475	0.0007123	1.44e-05	0.0012921	0.0008269	0.0003085	0.0003247	0.0003984	1.24e-05	0.1020763	0.6915539
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	0.0008850	0.0659807	0.0000914	0.0005366	0.0005228	4.38e-05	0.3312767	9.64e-05	1.17e-05	0.0001974	0.0014459	0.0022885	0.0001359	9.90e-05	0.0002148	0.0032408	0.0004747	0.0008538	0.0003700	0.0002398	0.0045655	0.0016578	0.5293436	0.0050574	0.0000809	4.3e-06	2.31e-05	0.0004364	2.10e-05	1.2e-06	0.0000631	0.0001898	1.62e-05	0.0006266	0.0368314	0.0041577	0.0017830	0.0008883	1.90e-05	0.0016916	0.0014957	0.0004095	0.0009973	0.0006252	7.90e-06	0.1393790	0.6258254
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14	0.0006897	0.0326727	0.0000492	0.0004253	0.0003318	3.34e-05	0.3263817	5.13e-05	5.40e-06	0.0001435	0.0011574	0.0019673	0.0000938	7.02e-05	0.0001652	0.0029204	0.0003675	0.0006011	0.0002872	0.0001810	0.0033125	0.0012911	0.5870258	0.0037180	0.0000624	4.1e-06	1.35e-05	0.0002005	1.45e-05	1.0e-06	0.0000378	0.0001458	8.00e-06	0.0004066	0.0271624	0.0028287	0.0010743	0.0006457	1.53e-05	0.0013084	0.0009681	0.0002973	0.0003279	0.0005188	1.61e-05	0.0865920	0.5559920
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	14	Tomato	Day 7	Tomato Day 7	0.0007324	0.0329202	0.0001398	0.0005384	0.0005593	5.41e-05	0.3461498	9.49e-05	1.41e-05	0.0001856	0.0013024	0.0023710	0.0001531	9.49e-05	0.0002730	0.0028512	0.0004519	0.0008048	0.0003529	0.0002988	0.0069066	0.0014564	0.5231174	0.0046147	0.0000508	4.2e-06	3.00e-05	0.0004095	2.00e-05	5.0e-06	0.0001148	0.0002006	1.58e-05	0.0004494	0.0597393	0.0046887	0.0015563	0.0010752	3.08e-05	0.0016628	0.0010478	0.0009662	0.0005509	0.0008405	9.57e-05	0.1307244	0.6617057
ShotgunWGS-ControlPig5GutMicrobiome-Day7	5	Control	Day 7	Control Day 7	0.0006564	0.0463444	0.0000557	0.0004617	0.0003835	3.61e-05	0.2599966	5.93e-05	7.20e-06	0.0001644	0.0012486	0.0020345	0.0001263	8.89e-05	0.0002123	0.0034222	0.0003796	0.0006821	0.0002920	0.0002165	0.0043266	0.0013153	0.6250284	0.0043172	0.0000687	2.6e-06	2.15e-05	0.0004359	2.60e-05	1.3e-06	0.0000524	0.0001573	8.80e-06	0.0005450	0.0371768	0.0035022	0.0014189	0.0007518	1.79e-05	0.0014739	0.0011206	0.0004307	0.0004457	0.0004802	4.20e-06	0.1149734	0.4159757
ShotgunWGS-TomatoPig18GutMicrobiome-Day7	18	Tomato	Day 7	Tomato Day 7	0.0007724	0.1016953	0.0000567	0.0003691	0.0004482	3.59e-05	0.3872593	6.68e-05	9.50e-06	0.0001245	0.0012329	0.0018259	0.0001234	5.10e-05	0.0001537	0.0041846	0.0002496	0.0006182	0.0002373	0.0001290	0.0027879	0.0011617	0.4453529	0.0028428	0.0001167	1.1e-06	1.40e-05	0.0001616	1.80e-05	6.0e-07	0.0000752	0.0001374	1.63e-05	0.0006148	0.0389241	0.0026521	0.0008313	0.0005133	1.46e-05	0.0009592	0.0013244	0.0008027	0.0002906	0.0007107	3.31e-05	0.1673878	0.8695561

# select only columns used for ANOVA
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.ForANOVA <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
  select(Pig, Diet, Time_Point, BtoF)

Repeated measures ANOVA of B to F ratio

BtoF.Ratio.ANOVA <- anova_test(data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF.ForANOVA, 
                          formula = BtoF ~ Diet*Time_Point + Error(Pig/(Time_Point)),
                          dv = BtoF, wid = Pig, between = Diet, within = Time_Point)

get_anova_table(BtoF.Ratio.ANOVA)

Effect	DFn	DFd	F	p	p<.05	ges
Diet	1	18	0.125	0.728		0.004
Time_Point	2	36	5.437	0.009	*	0.113
Diet:Time_Point	2	36	0.850	0.436		0.020

• Significant effect of time point (p = 0.009)
  
• Nonsignificant effect of diet (p = 0.728)
  
• Nonsignificant effect of diet:timepoint (p = 0.436)

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, grouping by time (both diets)

BtoF.Ratio.ANOVA.posthoc <- pairwise_t_test(BtoF ~ Time_Point, 
                                 data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF, 
                                 paired = TRUE, 
                                 p.adjust.method = "fdr") 

BtoF.Ratio.ANOVA.posthoc

.y.	group1	group2	n1	n2	statistic	df	p	p.adj	p.adj.signif
BtoF	Day 0	Day 7	20	20	1.418473	19	0.172	0.235	ns
BtoF	Day 0	Day 14	20	20	3.154634	19	0.005	0.016	*
BtoF	Day 7	Day 14	20	20	1.225553	19	0.235	0.235	ns

Significant difference is between day 0 and day 14 (padj = 0.016).

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, separating by diet

BtoF.Ratio.ANOVA.posthoc.bytime <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.ForANOVA %>%
  group_by(Diet) %>%
  pairwise_t_test(BtoF ~ Time_Point, 
                  paired = TRUE, 
                  p.adjust.method = "fdr")

BtoF.Ratio.ANOVA.posthoc.bytime

Diet	.y.	group1	group2	n1	n2	statistic	df	p	p.adj	p.adj.signif
Control	BtoF	Day 0	Day 7	10	10	1.3395140	9	0.213	0.320	ns
Control	BtoF	Day 0	Day 14	10	10	3.1870328	9	0.011	0.033	*
Control	BtoF	Day 7	Day 14	10	10	0.5885148	9	0.571	0.571	ns
Tomato	BtoF	Day 0	Day 7	10	10	0.4034028	9	0.696	0.696	ns
Tomato	BtoF	Day 0	Day 14	10	10	1.3470486	9	0.211	0.633	ns
Tomato	BtoF	Day 7	Day 14	10	10	0.8124311	9	0.438	0.657	ns

Significant in control between day 0 and 14, padj = 0.033. All else non-significant.

3.5.4 Bacteroidetes and Firmicutes

3.5.4.1 Plotting

Boxplotting

RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.OnlyBandF <- 
  RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long %>%
    filter(phylum == "Bacteroidetes" | phylum == "Firmicutes")

BandF_Boxplot <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.OnlyBandF %>%
  ggplot(aes(x=Diet, y=rel_abund, fill=Diet_By_Time_Point))+
  geom_boxplot(outlier.shape = NA)+
  geom_point(aes(fill = Diet_By_Time_Point), color = "black", position=position_jitterdodge(), alpha = 0.7) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4"))+
  ylim(0, 0.75) +
  theme_bw() +
  facet_wrap("phylum")+
  labs(x=NULL, y= "Relative Abundance", fill="Diet & Time Point") +
  theme(axis.text.x = element_text(size = 11, color = "black"), 
        axis.text.y = element_text(color = "black"), 
        panel.grid.minor = element_blank(), 
        strip.text.x = element_text(color = "black", size = 14),
        strip.background = element_rect(fill = "white"))

BandF_Boxplot

Saving

ggsave("Figures/BacteroidetesAndFirmicutes_Boxplot.png", 
       plot = BandF_Boxplot, 
       dpi = 800, 
       width = 7, 
       height = 5)

3.5.4.2 Statistics

3.5.4.2.1 Bacteroidetes

# select only columns used for ANOVA
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Bonly <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
  select(Pig, Diet, Time_Point, Bacteroidetes)

Bacteroidetes repeated measures ANOVA

Bacteroidetes.ANOVA <- anova_test(data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Bonly, 
                          formula = Bacteroidetes ~ Diet*Time_Point + Error(Pig/(Time_Point)),
                          dv = Bacteroidetes, wid = Pig, between = Diet, within = Time_Point)

get_anova_table(Bacteroidetes.ANOVA)

Effect	DFn	DFd	F	p	p<.05	ges
Diet	1	18	0.009	0.928		0.000272
Time_Point	2	36	4.131	0.024	*	0.089000
Diet:Time_Point	2	36	0.700	0.503		0.016000

• Significant effect of time point (p = 0.024)
  
• Nonsignificant effect of diet (p = 0.928)
  
• onsignificant effect of diet:timepoint (p = 0.503)

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, grouping by time (both diets)

Bacteroidetes.ANOVA.posthoc <- pairwise_t_test(Bacteroidetes ~ Time_Point, 
                                 data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF, 
                                 paired = TRUE, 
                                 p.adjust.method = "fdr") 

Bacteroidetes.ANOVA.posthoc

.y.	group1	group2	n1	n2	statistic	df	p	p.adj	p.adj.signif
Bacteroidetes	Day 0	Day 7	20	20	1.6206873	19	0.122	0.183	ns
Bacteroidetes	Day 0	Day 14	20	20	2.5573425	19	0.019	0.058	ns
Bacteroidetes	Day 7	Day 14	20	20	0.5724219	19	0.574	0.574	ns

Borderline significant difference is between day 0 and day 14 (padj = 0.058).

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, separating by diet

Bacteroidetes.ANOVA.posthoc.bytime <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Bonly %>%
  group_by(Diet) %>%
  pairwise_t_test(Bacteroidetes ~ Time_Point, 
                  paired = TRUE, 
                  p.adjust.method = "fdr")

Bacteroidetes.ANOVA.posthoc.bytime

Diet	.y.	group1	group2	n1	n2	statistic	df	p	p.adj	p.adj.signif
Control	Bacteroidetes	Day 0	Day 7	10	10	1.5566013	9	0.154	0.231	ns
Control	Bacteroidetes	Day 0	Day 14	10	10	3.0185434	9	0.015	0.044	*
Control	Bacteroidetes	Day 7	Day 14	10	10	0.1517949	9	0.883	0.883	ns
Tomato	Bacteroidetes	Day 0	Day 7	10	10	0.4864046	9	0.638	0.638	ns
Tomato	Bacteroidetes	Day 0	Day 14	10	10	1.0815222	9	0.308	0.638	ns
Tomato	Bacteroidetes	Day 7	Day 14	10	10	0.4945637	9	0.633	0.638	ns

Significant difference in control between day 0 and 14, padj = 0.044. All else non-significant.

3.5.4.2.2 Firmicutes

# select only columns used for ANOVA
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Fonly <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
  select(Pig, Diet, Time_Point, Firmicutes)

Firmicutes repeated measures ANOVA

Firmicutes.ANOVA <- anova_test(data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Fonly, 
                          formula = Firmicutes ~ Diet*Time_Point + Error(Pig/(Time_Point)),
                          dv = Firmicutes, wid = Pig, between = Diet, within = Time_Point)

get_anova_table(Firmicutes.ANOVA)

Effect	DFn	DFd	F	p	p<.05	ges
Diet	1	18	1.079	0.313		0.033
Time_Point	2	36	8.102	0.001	*	0.161
Diet:Time_Point	2	36	0.993	0.380		0.023

• Significant effect of time point (p = 0.001)
  
• Nonsignificant effect of diet (p = 0.313)
  
• Nonsignificant effect of diet:timepoint (p = 0.380)

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, grouping by time (both diets)

Firmicutes.ANOVA.posthoc <- pairwise_t_test(Firmicutes ~ Time_Point, 
                                 data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF, 
                                 paired = TRUE, 
                                 p.adjust.method = "fdr") 

Firmicutes.ANOVA.posthoc

.y.	group1	group2	n1	n2	statistic	df	p	p.adj	p.adj.signif
Firmicutes	Day 0	Day 7	20	20	-1.602836	19	0.125000	0.125	ns
Firmicutes	Day 0	Day 14	20	20	-3.926193	19	0.000907	0.003	**
Firmicutes	Day 7	Day 14	20	20	-1.672304	19	0.111000	0.125	ns

Significant difference is between day 0 and day 14 (padj = 0.003).

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, separating by diet

Firmicutes.ANOVA.posthoc.bytime <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Fonly %>%
  group_by(Diet) %>%
  pairwise_t_test(Firmicutes ~ Time_Point, 
                  paired = TRUE, 
                  p.adjust.method = "fdr")

Firmicutes.ANOVA.posthoc.bytime

Diet	.y.	group1	group2	n1	n2	statistic	df	p	p.adj	p.adj.signif
Control	Firmicutes	Day 0	Day 7	10	10	-1.4794417	9	0.173	0.259	ns
Control	Firmicutes	Day 0	Day 14	10	10	-3.2515420	9	0.010	0.030	*
Control	Firmicutes	Day 7	Day 14	10	10	-0.8427752	9	0.421	0.421	ns
Tomato	Firmicutes	Day 0	Day 7	10	10	-0.5497215	9	0.596	0.596	ns
Tomato	Firmicutes	Day 0	Day 14	10	10	-1.9580266	9	0.082	0.246	ns
Tomato	Firmicutes	Day 7	Day 14	10	10	-1.0468992	9	0.322	0.483	ns

Significant difference is in control between day 0 and 14, padj = 0.030. All else non-significant.

3.6 Alpha Diversity

Calculated alpha diversity of phyla based on relative abundance, including all the filtering for implausible phyla and removing samples with more than 33.33% missing samples

3.6.1 Wrangling

dim(RelAbund.Phyla.Filt.zerofilt)

## [1] 60 50

RelAbund.Phyla.Filt.zerofilt[1:5,1:10]

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	Acidobacteria	Actinobacteria	Apicomplexa	Aquificae	Ascomycota
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	0.0007406	0.0481336	0.0000595	0.0005033	0.0003842
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	0.0008850	0.0659807	0.0000914	0.0005366	0.0005228
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14	0.0006897	0.0326727	0.0000492	0.0004253	0.0003318
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	14	Tomato	Day 7	Tomato Day 7	0.0007324	0.0329202	0.0001398	0.0005384	0.0005593
ShotgunWGS-ControlPig5GutMicrobiome-Day7	5	Control	Day 7	Control Day 7	0.0006564	0.0463444	0.0000557	0.0004617	0.0003835

Wrangle

# move Sample_Name to rownames, remove metadata
RelAbund.Phyla.Filt.zerofilt.alphadiv <- RelAbund.Phyla.Filt.zerofilt

rownames(RelAbund.Phyla.Filt.zerofilt.alphadiv) <- RelAbund.Phyla.Filt.zerofilt.alphadiv$Sample_Name  

## Warning: Setting row names on a tibble is deprecated.

# remove metadata
RelAbund.Phyla.Filt.zerofilt.alphadiv <- RelAbund.Phyla.Filt.zerofilt.alphadiv %>%
  select(Acidobacteria:ncol(.))

RelAbund.Phyla.Filt.zerofilt.alphadiv[1:5,1:5]

Acidobacteria	Actinobacteria	Apicomplexa	Aquificae	Ascomycota
0.0007406	0.0481336	0.0000595	0.0005033	0.0003842
0.0008850	0.0659807	0.0000914	0.0005366	0.0005228
0.0006897	0.0326727	0.0000492	0.0004253	0.0003318
0.0007324	0.0329202	0.0001398	0.0005384	0.0005593
0.0006564	0.0463444	0.0000557	0.0004617	0.0003835

3.6.2 Calculate alpha diversity

# run alpha diversity on phyla
phyla.filt.div <- diversity(RelAbund.Phyla.Filt.zerofilt.alphadiv, index = "shannon")

# convert to df
phyla.filt.div.df <- as.data.frame(phyla.filt.div)

# make column name 'shannon.phyla.filt'
colnames(phyla.filt.div.df) <- "shannon.phyla.filt"

head(phyla.filt.div.df)

shannon.phyla.filt
1.122832
1.231877
1.062241
1.221226
1.108308
1.261677

Combine with metadata

# combine with metadata
phyla.filt.div.df.meta <- cbind(RelAbund.Phyla.Filt.zerofilt[,1:5], phyla.filt.div.df)

head(phyla.filt.div.df.meta)

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	shannon.phyla.filt
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	1.122832
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	1.231877
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14	1.062241
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	14	Tomato	Day 7	Tomato Day 7	1.221226
ShotgunWGS-ControlPig5GutMicrobiome-Day7	5	Control	Day 7	Control Day 7	1.108308
ShotgunWGS-TomatoPig18GutMicrobiome-Day7	18	Tomato	Day 7	Tomato Day 7	1.261677

3.6.3 Plotting

X-axis by diet

alpha.diversity.phyla.bydiet <- phyla.filt.div.df.meta %>%
  ggplot(aes(x = Diet, y = shannon.phyla.filt, fill = Diet_By_Time_Point)) +
  geom_boxplot(outlier.shape = NA) +
  geom_point(aes(fill = Diet_By_Time_Point), 
             color = "black", 
             alpha = 0.7, 
             position=position_jitterdodge()) +
  scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, color = "black")) +
  labs(x=NULL, 
       y="Shannon diversity index", 
       title = "Alpha Diversity",
       subtitle = "Shannon Index, Phyla Level", 
       fill="Diet & Time Point")

alpha.diversity.phyla.bydiet

ggsave("Figures/AlphaDiversityPhyla_ByDiet_Boxplot.png", 
       plot = alpha.diversity.phyla.bydiet, 
       dpi = 800, 
       width = 10, 
       height = 6)

X-axis by time point

alpha.diversity.phyla.bytime <- phyla.filt.div.df.meta %>%
  ggplot(aes(x = Time_Point, y = shannon.phyla.filt, fill = Diet_By_Time_Point)) +
  geom_boxplot(outlier.shape = NA) +
  geom_point(color = "black", alpha = 0.7, position=position_jitterdodge()) +
  scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, color = "black")) +
  labs(x=NULL, 
       y="Shannon diversity index", 
       title = "Alpha Diversity",
       subtitle = "Shannon Index, Phyla Level", 
       fill="Diet & Time Point")

alpha.diversity.phyla.bytime

ggsave("Figures/AlphaDiversityPhyla_ByTime_Boxplot.png", 
       plot = alpha.diversity.phyla.bytime, 
       dpi = 800, 
       width = 7, 
       height = 5)

3.6.4 Statistics

Repeated measures ANOVA

# must remove columns that aren't used in anova
head(phyla.filt.div.df.meta) 

Sample_Name	Pig	Diet	Time_Point	Diet_By_Time_Point	shannon.phyla.filt
ShotgunWGS-ControlPig6GutMicrobiome-Day14	6	Control	Day 14	Control Day 14	1.122832
ShotgunWGS-ControlPig8GutMicrobiome-Day0	8	Control	Day 0	Control Day 0	1.231877
ShotgunWGS-ControlPig3GutMicrobiome-Day14	3	Control	Day 14	Control Day 14	1.062241
ShotgunWGS-TomatoPig14GutMicrobiome-Day7	14	Tomato	Day 7	Tomato Day 7	1.221226
ShotgunWGS-ControlPig5GutMicrobiome-Day7	5	Control	Day 7	Control Day 7	1.108308
ShotgunWGS-TomatoPig18GutMicrobiome-Day7	18	Tomato	Day 7	Tomato Day 7	1.261677

phyla.filt.div.foranova <- phyla.filt.div.df.meta[,-c(1,5)]

head(phyla.filt.div.foranova)

Pig	Diet	Time_Point	shannon.phyla.filt
6	Control	Day 14	1.122832
8	Control	Day 0	1.231877
3	Control	Day 14	1.062241
14	Tomato	Day 7	1.221226
5	Control	Day 7	1.108308
18	Tomato	Day 7	1.261677

phyla.filt.alphadiv.anova <- 
  anova_test(data = phyla.filt.div.foranova,
             formula = shannon.phyla.filt ~ Diet*Time_Point + Error(Pig/Time_Point),
             dv = shannon.phyla.filt, 
             wid = Pig, 
             within = Time_Point, 
             between = Diet)

get_anova_table(phyla.filt.alphadiv.anova)

Effect	DFn	DFd	F	p	p<.05	ges
Diet	1	18	10.767	0.004	*	0.158
Time_Point	2	36	2.628	0.086		0.091
Diet:Time_Point	2	36	0.236	0.791		0.009

• Significant effect of diet (p = 0.004)
  
• Non-significant effect of timepoint (0.086)
  
• Non-significant interaction of diet:time point (p = 0.791).

Check for normality

shapiro.test(phyla.filt.div.df.meta$shannon.phyla.filt)

## 
##  Shapiro-Wilk normality test
## 
## data:  phyla.filt.div.df.meta$shannon.phyla.filt
## W = 0.99022, p-value = 0.9135

Normal.

Post-hoc tests

posthoc.morevariables <- phyla.filt.div.foranova %>%
  group_by(Time_Point) %>%
  anova_test(dv = shannon.phyla.filt, wid = Pig, between = Diet) %>%
  get_anova_table() %>%
  adjust_pvalue(method = "fdr")

## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()

posthoc.morevariables

Time_Point	Effect	DFn	DFd	F	p	p<.05	ges	p.adj
Day 0	Diet	1	18	1.219	0.284		0.063	0.284
Day 7	Diet	1	18	3.654	0.072		0.169	0.108
Day 14	Diet	1	18	8.743	0.008	*	0.327	0.024

Significant effect of diet at day 14 (padj = 0.024)

posthoc.evenmorespecific <- phyla.filt.div.foranova %>%
  group_by(Time_Point) %>%
  pairwise_t_test(shannon.phyla.filt ~ Diet,
                  paired = TRUE,
                  p.adjust.method = "fdr")

posthoc.evenmorespecific

Time_Point	.y.	group1	group2	n1	n2	statistic	df	p	p.adj	p.adj.signif
Day 0	shannon.phyla.filt	Control	Tomato	10	10	-1.033643	9	0.328	0.328	ns
Day 7	shannon.phyla.filt	Control	Tomato	10	10	-1.934233	9	0.085	0.085	ns
Day 14	shannon.phyla.filt	Control	Tomato	10	10	-3.211219	9	0.011	0.011	*

• Significant effect between control and tomato at day 14 (padj = 0.01)
  
• Nonsignificant at day 0 (padj = 0.328)
  
• Nonsignificant but getting close at day 7 (padj = 0.085)

3.7 ALDEx2

Quick introduction to anatomy of the aldex function

The aldex function does every step - data transformation and statistics
variable.name <- aldex(reads.data, variables.vector, mc.samples=#, test=“t”/“kw”, effect=T/F)
reads.data - your reads/count data, un changed
variables.vector - a vector of the variables corresponding to sample groups, in SAME order as sample names (and therefore columns)
mc.samples - here you tell the function how many Monte Carlo sampels to use with an integer (128 is typical)
test - which test do you want, t-test and wilcoxon, or anova-like and kruskal wallace? (will always do the parametric and non-parametric) t = t-test and wilcoxon kw = anova-like and kruskal wallace
effect - do you want it to incude effect results in output?

Key to aldex outputs - taken directly from vignette

• we.ep - Expected P value of Welch’s t test
• we.eBH - Expected Benjamini-Hochberg corrected P value of Welch’s t test
  
• wi.ep - Expected P value of Wilcoxon rank test
• wi.eBH - Expected Benjamini-Hochberg corrected P value of Wilcoxon test
• kw.ep - Expected P value of Kruskal-Wallace test
• kw.eBH - Expected Benjamini-Hochberg corrected P value of Kruskal-Wallace test
• glm.ep - Expected P value of glm test
• glm.eBH - Expected Benjamini-Hochberg corrected P value of glm test
• rab.all - median clr value for all samples in the feature
• rab.win.NS - median clr value for the NS group of samples
• rab.win.S - median clr value for the S group of samples
• dif.btw - median difference in clr values between S and NS groups
• dif.win - median of the largest difference in clr values within S and NS groups
• effect - median effect size: diff.btw / max(diff.win) for all instances
• overlap - proportion of effect size that overlaps 0 (i.e. no effect)

ALDEx2 takes counts, not relative abundance.

We are using Benjamini Hochberg corrected pvalues, or we.eBH for t-tests (i.e., subsetting by time), and Benjamini-Hochberg corrected pvalues of the glm test glm.eBH for ANOVA tests (i.e., subsetting by diet)

Downloading ALDEx2

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

BiocManager::install("ALDEx2")

3.7.1 Wrangling

Since we use counts for ALDEx2, we need to filter our counts data to include only the phyla we ended up using in our final analysis

# this data set filtered to remove inplausible phyla, but still includes phyla with a lot of missing values 
Phyla.Counts.Filt[1:10,1:10]

domain	phylum	ShotgunWGS-ControlPig6GutMicrobiome-Day14	ShotgunWGS-ControlPig8GutMicrobiome-Day0	ShotgunWGS-ControlPig3GutMicrobiome-Day14	ShotgunWGS-TomatoPig14GutMicrobiome-Day7	ShotgunWGS-ControlPig5GutMicrobiome-Day7	ShotgunWGS-TomatoPig18GutMicrobiome-Day7	ShotgunWGS-TomatoPig16GutMicrobiome-Day7	ShotgunWGS-ControlPig10GutMicrobiome-Day7
Bacteria	Acidobacteria	2874	3717	2663	880	2016	1377	1570	1298
Bacteria	Actinobacteria	186789	277130	126155	39557	142345	181295	58263	109273
Eukaryota	Apicomplexa	231	384	190	168	171	101	224	287
Bacteria	Aquificae	1953	2254	1642	647	1418	658	892	900
Eukaryota	Ascomycota	1491	2196	1281	672	1178	799	1720	763
Eukaryota	Bacillariophyta	105	184	129	65	111	64	80	65
Bacteria	Bacteroidetes	1424565	1391417	1260217	415935	798569	690378	826292	757469
Eukaryota	Basidiomycota	240	405	198	114	182	119	199	111
Eukaryota	Blastocladiomycota	0	0	0	0	1	0	0	0
Bacteria	Candidatus Poribacteria	26	49	21	17	22	17	21	23

dim(Phyla.Counts.Filt)

## [1] 53 62

# final phyla list, after filtering for number of zeros
final_phyla[1:10,]

##  [1] "Acidobacteria"           "Actinobacteria"         
##  [3] "Apicomplexa"             "Aquificae"              
##  [5] "Ascomycota"              "Bacillariophyta"        
##  [7] "Bacteroidetes"           "Basidiomycota"          
##  [9] "Candidatus Poribacteria" "Chlamydiae"

# how many final phyla do we have?
dim(final_phyla)

## [1] 45  1

# join to create a df with phyla in rows, samples in columns
# filtered for genera used in this analysis
phyla_counts_foraldex <- inner_join(final_phyla, Phyla.Counts.Filt,
                                    by = "phylum")

dim(phyla_counts_foraldex)

## [1] 45 62

phyla_counts_foraldex[1:10, 1:4]

phylum	domain	ShotgunWGS-ControlPig6GutMicrobiome-Day14	ShotgunWGS-ControlPig8GutMicrobiome-Day0
Acidobacteria	Bacteria	2874	3717
Actinobacteria	Bacteria	186789	277130
Apicomplexa	Eukaryota	231	384
Aquificae	Bacteria	1953	2254
Ascomycota	Eukaryota	1491	2196
Bacillariophyta	Eukaryota	105	184
Bacteroidetes	Bacteria	1424565	1391417
Basidiomycota	Eukaryota	240	405
Candidatus Poribacteria	Bacteria	26	49
Chlamydiae	Bacteria	552	829

# add phyla as rownames
rownames(phyla_counts_foraldex) <- phyla_counts_foraldex$phylum

# remove phylum, domain as columns for cleaner data
phyla_counts_foraldex <- phyla_counts_foraldex %>%
  select(-phylum, -domain)

3.7.2 Subset by Time

3.7.2.1 Day 0

# subset day 0 only
Day0.Counts.Phyla.filt <- phyla_counts_foraldex %>% 
  select(ends_with("Day0"))

ALDEx2 function needs a factor of variables

# order alphabetically so making the meta data vector is easier
Day0.Counts.Phyla.filt <- Day0.Counts.Phyla.filt[order(colnames(Day0.Counts.Phyla.filt))]

Diets.Day0.Phyla <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day0.Phyla

##  [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
##  [8] "Control" "Control" "Control" "Tomato"  "Tomato"  "Tomato"  "Tomato" 
## [15] "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"

Run t-tests

# runs very slowly
# set cache = TRUE to save results
filt.Phyla.Day0.ByDiet.aldex <- aldex(Day0.Counts.Phyla.filt, 
                                       Diets.Day0.Phyla, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)

## aldex.clr: generating Monte-Carlo instances and clr values

## operating in serial mode

## computing center with all features

## aldex.ttest: doing t-test

## aldex.effect: calculating effect sizes

filt.Phyla.Day0.ByDiet.aldex <- 
  filt.Phyla.Day0.ByDiet.aldex[order(filt.Phyla.Day0.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Phyla.Day0.ByDiet.aldex))

	rab.all	rab.win.Control	rab.win.Tomato	diff.btw	diff.win	effect	overlap	we.ep	we.eBH	wi.ep	wi.eBH
Proteobacteria	6.5844743	6.3124220	6.7529025	0.3751461	0.4495074	0.8597605	0.1690000	0.0084467	0.3231164	0.0112160	0.4107197
unclassified (derived from Eukaryota)	0.4413930	0.9017697	0.3698077	-0.4796739	0.3863639	-0.8726403	0.2336000	0.0252432	0.4111961	0.0498007	0.5579480
Chlorophyta	-1.5941761	-1.6641747	-1.5137163	0.1707321	0.3129495	0.5107920	0.2665467	0.0679638	0.5366056	0.1071872	0.6198038
Tenericutes	0.9370078	1.0451421	0.8892908	-0.1603222	0.2045020	-0.6648997	0.2579484	0.0669788	0.5517365	0.0847836	0.6117565
Basidiomycota	-2.2192649	-2.1309025	-2.3154649	-0.1768920	0.2941272	-0.5641660	0.2661468	0.0954968	0.5750007	0.1102669	0.6115027
Elusimicrobia	-0.7868217	-0.8527568	-0.7342933	0.1256919	0.2372767	0.4710149	0.2729454	0.0975078	0.6025237	0.1318675	0.6467929

No significantly different phyla

hist(filt.Phyla.Day0.ByDiet.aldex$we.eBH,
     breaks = 45,
     main = "Histogram of p-values on the effect of diet at day 0 on phyla",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")

we.eBH is the Benjamini-Hochberg corrected p-value, and nothing is < 0.05

3.7.2.2 Day 7

# subset day 7 only
Day7.Counts.Phyla.filt <- phyla_counts_foraldex %>% 
  select(ends_with("Day7"))

ALDEx2 function needs a factor of variables

# order alphabetically so making the meta data vector is easier
Day7.Counts.Phyla.filt <- Day7.Counts.Phyla.filt[order(colnames(Day7.Counts.Phyla.filt))]

Diets.Day7.Phyla <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day7.Phyla

##  [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
##  [8] "Control" "Control" "Control" "Tomato"  "Tomato"  "Tomato"  "Tomato" 
## [15] "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"

Run t-tests

# runs very slowly
# set cache = TRUE to save results
filt.Phyla.Day7.ByDiet.aldex <- aldex(Day7.Counts.Phyla.filt, 
                                       Diets.Day7.Phyla, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)

## aldex.clr: generating Monte-Carlo instances and clr values

## operating in serial mode

## computing center with all features

## aldex.ttest: doing t-test

## aldex.effect: calculating effect sizes

filt.Phyla.Day7.ByDiet.aldex <- 
  filt.Phyla.Day7.ByDiet.aldex[order(filt.Phyla.Day7.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Phyla.Day7.ByDiet.aldex))

	rab.all	rab.win.Control	rab.win.Tomato	diff.btw	diff.win	effect	overlap	we.ep	we.eBH	wi.ep	wi.eBH
unclassified (derived from Bacteria)	0.6388532	0.3079673	1.0287140	0.6658131	0.3791505	1.5869329	0.0484000	0.0000600	0.0026939	0.0002064	0.0092214
Chrysiogenetes	-2.2752377	-2.1607944	-2.4320925	-0.2736389	0.3606273	-0.7276788	0.2079584	0.0440744	0.3391195	0.0481118	0.3016668
Ascomycota	0.2284804	0.1345162	0.3826435	0.2367420	0.4130549	0.5294957	0.2371526	0.0426930	0.4064056	0.0592643	0.3506656
Firmicutes	10.4272112	10.6439349	10.2709149	-0.3338018	0.4195976	-0.6936920	0.2095581	0.0414231	0.4154189	0.0292173	0.2974564
Basidiomycota	-2.1982535	-2.3486173	-2.0889663	0.2581045	0.4229414	0.5313093	0.2490000	0.0801855	0.4613875	0.0843152	0.3967393
unclassified (derived from Viruses)	0.7560291	0.6059765	0.9008385	0.2971234	0.5195647	0.4858989	0.2028000	0.0825219	0.4957042	0.0295864	0.2750966

One phyla was significantly different by diet at day 7 - unclassified (derived from bacteria), padj = 0.002

hist(filt.Phyla.Day7.ByDiet.aldex$we.eBH,
     breaks = 45,
     main = "Histogram of p-values on the effect of diet at day 7 on phyla",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")

3.7.2.3 Day 14

# subset day 14 only
Day14.Counts.Phyla.filt <- phyla_counts_foraldex %>% 
  select(ends_with("Day14"))

ALDEx2 function needs a factor of variables

# order alphabetically so making the meta data vector is easier
Day14.Counts.Phyla.filt <- Day14.Counts.Phyla.filt[order(colnames(Day14.Counts.Phyla.filt))]

Diets.Day14.Phyla <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day14.Phyla

##  [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
##  [8] "Control" "Control" "Control" "Tomato"  "Tomato"  "Tomato"  "Tomato" 
## [15] "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"

Run t-tests

filt.Phyla.Day14.ByDiet.aldex <- aldex(Day14.Counts.Phyla.filt, 
                                       Diets.Day14.Phyla, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)

## aldex.clr: generating Monte-Carlo instances and clr values

## operating in serial mode

## computing center with all features

## aldex.ttest: doing t-test

## aldex.effect: calculating effect sizes

filt.Phyla.Day14.ByDiet.aldex <- 
  filt.Phyla.Day14.ByDiet.aldex[order(filt.Phyla.Day14.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Phyla.Day14.ByDiet.aldex))

	rab.all	rab.win.Control	rab.win.Tomato	diff.btw	diff.win	effect	overlap	we.ep	we.eBH	wi.ep	wi.eBH
unclassified (derived from Bacteria)	0.3955839	0.0637665	0.9936172	0.9138840	0.3440659	2.5329865	0.0000140	0.0000029	0.0001296	0.0000108	0.0004508
Nematoda	-2.6707903	-2.9593902	-2.2677530	0.7354907	0.5338348	1.3576352	0.0387923	0.0006428	0.0086327	0.0003724	0.0043708
Apicomplexa	-2.0634494	-2.4245375	-1.5557619	0.7988599	0.6165689	1.2337851	0.0893821	0.0011181	0.0132974	0.0015125	0.0147011
Deinococcus-Thermus	0.8939871	0.9662293	0.7931857	-0.1778881	0.1477943	-1.1349945	0.0983803	0.0056093	0.0312997	0.0048787	0.0286602
Proteobacteria	6.4735959	6.4211764	6.6009225	0.1934713	0.2120727	0.9519697	0.1331734	0.0065513	0.0406465	0.0059788	0.0354072
Firmicutes	10.6378646	10.6953205	10.4005857	-0.3135918	0.3299862	-0.8737743	0.1683663	0.0101642	0.0582035	0.0130374	0.0638702

hist(filt.Phyla.Day14.ByDiet.aldex$we.eBH,
     breaks = 45,
     main = "Histogram of p-values on the effect of diet at day 14 on phyla",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")

How many significant phyla are there?

filt.Phyla.Day14.ByDiet.aldex.sig <- 
  filt.Phyla.Day14.ByDiet.aldex[which(filt.Phyla.Day14.ByDiet.aldex$we.eBH<0.05),]

length(rownames(filt.Phyla.Day14.ByDiet.aldex.sig))

## [1] 5

5 sig phyla

Which phyla are they?

sig_day14_phyla_aldex2 <- as.data.frame(cbind(rownames(filt.Phyla.Day14.ByDiet.aldex.sig),
                                 filt.Phyla.Day14.ByDiet.aldex.sig$we.eBH))

sig_day14_phyla_aldex2

V1	V2
unclassified (derived from Bacteria)	0.00012957252663489
Nematoda	0.00863266922128627
Apicomplexa	0.0132974068631904
Deinococcus-Thermus	0.0312997000489172
Proteobacteria	0.0406465169788867

• unclassified (derived from Bacteria)
• Nematoda
• Apicomplexa
• Deinococcus-Thermus
• Proteobacteria

What is the directionality of the change?

filt.Phyla.Day14.ByDiet.aldex %>%
  select(rab.win.Control, rab.win.Tomato, we.eBH) %>%
  filter(we.eBH <= 0.05)

	rab.win.Control	rab.win.Tomato	we.eBH
unclassified (derived from Bacteria)	0.0637665	0.9936172	0.0001296
Nematoda	-2.9593902	-2.2677530	0.0086327
Apicomplexa	-2.4245375	-1.5557619	0.0132974
Deinococcus-Thermus	0.9662293	0.7931857	0.0312997
Proteobacteria	6.4211764	6.6009225	0.0406465

Higher in control:
* Deinococcus-Thermus

Higher in tomato:
* unclassified (derived from Bacteria)
* Nematoda
* Apicomplexa
* Proteobacteria

3.7.3 Subset by diet

3.7.3.1 Control

# subset control only samples across all time points, should be n=30
Control.Counts.Phyla.filt <- phyla_counts_foraldex %>% 
  select(contains("Control"))

ALDEx2 function needs a factor of variables

# results in pigs at different time points being grouped together
Control.Counts.Phyla.filt <- Control.Counts.Phyla.filt[order(colnames(Control.Counts.Phyla.filt))]

# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc

TimePoints.Control.Phyla <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))

# check and make sure it looks right
TimePoints.Control.Phyla

##  [1] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [10] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [19] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [28] "Day0"  "Day14" "Day7"

More than two conditions this time, use the ANOVA-like test

filt.Phyla.Control.ByTime.aldex <- aldex(Control.Counts.Phyla.filt, 
                                          TimePoints.Control.Phyla, 
                                          mc.samples = 1000, 
                                          test = "kw", 
                                          effect = FALSE)

## aldex.clr: generating Monte-Carlo instances and clr values

## operating in serial mode

## computing center with all features

## aldex.glm: doing Kruskal-Wallace and glm test (ANOVA-like)

## operating in serial mode

We are looking at glm.eBH for the BH corrected ANOVA pval

filt.Phyla.Control.ByTime.aldex <- 
  filt.Phyla.Control.ByTime.aldex[order(filt.Phyla.Control.ByTime.aldex$glm.eBH, 
                                         decreasing = FALSE),]

kable(head(filt.Phyla.Control.ByTime.aldex))

	kw.ep	kw.eBH	glm.ep	glm.eBH
unclassified (derived from Bacteria)	0.0112045	0.1823791	0.0028685	0.0787967
unclassified (derived from Eukaryota)	0.0508419	0.2491934	0.0082087	0.1223714
Dictyoglomi	0.0252249	0.2054731	0.0217191	0.1438237
Firmicutes	0.0333238	0.2218384	0.0239034	0.1533695
Verrucomicrobia	0.0392678	0.2305099	0.0265263	0.1536415
Fibrobacteres	0.2603513	0.5233890	0.0315004	0.1584977

hist(filt.Phyla.Control.ByTime.aldex$glm.eBH,
     breaks = 45,
     main = "Histogram of p-values on the effect of time on control pigs on phyla",
     xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")

How many significant phyla are there?

filt.Phyla.Control.ByTime.aldex.sig <- 
  filt.Phyla.Control.ByTime.aldex[which(filt.Phyla.Control.ByTime.aldex$glm.eBH<0.05),]

length(rownames(filt.Phyla.Control.ByTime.aldex.sig))

## [1] 0

0 sig phyla

3.7.3.2 Tomato

# subset tomato only samples across all time points, should be n=30
Tomato.Counts.Phyla.filt <- phyla_counts_foraldex %>% 
  select(contains("Tomato"))

ALDEx2 function needs a factor of variables

# results in pigs at different time points being grouped together
Tomato.Counts.Phyla.filt <- Tomato.Counts.Phyla.filt[order(colnames(Tomato.Counts.Phyla.filt))]

# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc

TimePoints.Tomato.Phyla <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))

# check and make sure it looks right
TimePoints.Tomato.Phyla

##  [1] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [10] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [19] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [28] "Day0"  "Day14" "Day7"

More than two conditions this time, use the ANOVA-like test

filt.Phyla.Tomato.ByTime.aldex <- aldex(Tomato.Counts.Phyla.filt, 
                                          TimePoints.Tomato.Phyla, 
                                          mc.samples = 1000, 
                                          test = "kw", 
                                          effect = FALSE)

## aldex.clr: generating Monte-Carlo instances and clr values

## operating in serial mode

## computing center with all features

## aldex.glm: doing Kruskal-Wallace and glm test (ANOVA-like)

## operating in serial mode

We are looking at glm.eBH for the BH corrected ANOVA pval

filt.Phyla.Tomato.ByTime.aldex <- 
  filt.Phyla.Tomato.ByTime.aldex[order(filt.Phyla.Tomato.ByTime.aldex$glm.eBH, 
                                         decreasing = FALSE),]

kable(head(filt.Phyla.Tomato.ByTime.aldex))

	kw.ep	kw.eBH	glm.ep	glm.eBH
unclassified (derived from Bacteria)	0.0015051	0.0576720	0.0000094	0.0004234
Tenericutes	0.0048770	0.1034019	0.0060061	0.1129962
Aquificae	0.0377166	0.3542496	0.0273776	0.2797142
Hemichordata	0.1870476	0.5808706	0.1198649	0.4897095
Basidiomycota	0.1315116	0.5753463	0.1059579	0.5545839
Ascomycota	0.1949579	0.6836709	0.0930040	0.6006795

hist(filt.Phyla.Tomato.ByTime.aldex$glm.eBH,
     breaks = 45,
     main = "Histogram of p-values on the effect of time on tomato pigs on phyla",
     xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")

How many significant phyla are there?

filt.Phyla.Tomato.ByTime.aldex.sig <- 
  filt.Phyla.Tomato.ByTime.aldex[which(filt.Phyla.Tomato.ByTime.aldex$glm.eBH<0.05),]

length(rownames(filt.Phyla.Tomato.ByTime.aldex.sig))

## [1] 1

1 sig phyla, unclassified (derived from Bacteria)